Overview

Dataset statistics

Number of variables110
Number of observations1099444
Missing cells58591168
Missing cells (%)48.4%
Total size in memory922.7 MiB
Average record size in memory880.0 B

Variable types

Numeric66
Text19
Unsupported25

Alerts

year has constant value ""Constant
quarter has constant value ""Constant
flights has constant value ""Constant
deptime has 16322 (1.5%) missing valuesMissing
depdelay has 16335 (1.5%) missing valuesMissing
depdelayminutes has 16335 (1.5%) missing valuesMissing
depdel15 has 16335 (1.5%) missing valuesMissing
departuredelaygroups has 16335 (1.5%) missing valuesMissing
taxiout has 16558 (1.5%) missing valuesMissing
wheelsoff has 16558 (1.5%) missing valuesMissing
wheelson has 17189 (1.6%) missing valuesMissing
taxiin has 17189 (1.6%) missing valuesMissing
arrtime has 17189 (1.6%) missing valuesMissing
arrdelay has 18860 (1.7%) missing valuesMissing
arrdelayminutes has 18860 (1.7%) missing valuesMissing
arrdel15 has 18860 (1.7%) missing valuesMissing
arrivaldelaygroups has 18860 (1.7%) missing valuesMissing
cancellationcode has 1082691 (98.5%) missing valuesMissing
actualelapsedtime has 18860 (1.7%) missing valuesMissing
airtime has 18860 (1.7%) missing valuesMissing
carrierdelay has 898621 (81.7%) missing valuesMissing
weatherdelay has 898621 (81.7%) missing valuesMissing
nasdelay has 898621 (81.7%) missing valuesMissing
securitydelay has 898621 (81.7%) missing valuesMissing
lateaircraftdelay has 898621 (81.7%) missing valuesMissing
firstdeptime has 1094184 (99.5%) missing valuesMissing
totaladdgtime has 1094187 (99.5%) missing valuesMissing
longestaddgtime has 1094187 (99.5%) missing valuesMissing
divreacheddest has 1097337 (99.8%) missing valuesMissing
divactualelapsedtime has 1097773 (99.8%) missing valuesMissing
divarrdelay has 1097773 (99.8%) missing valuesMissing
divdistance has 1097337 (99.8%) missing valuesMissing
div1airport has 1097142 (99.8%) missing valuesMissing
div1airportid has 1097142 (99.8%) missing valuesMissing
div1airportseqid has 1097142 (99.8%) missing valuesMissing
div1wheelson has 1097142 (99.8%) missing valuesMissing
div1totalgtime has 1097142 (99.8%) missing valuesMissing
div1longestgtime has 1097142 (99.8%) missing valuesMissing
div1wheelsoff has 1097752 (99.8%) missing valuesMissing
div1tailnum has 1097752 (99.8%) missing valuesMissing
div2airport has 1099417 (> 99.9%) missing valuesMissing
div2airportid has 1099417 (> 99.9%) missing valuesMissing
div2airportseqid has 1099417 (> 99.9%) missing valuesMissing
div2wheelson has 1099417 (> 99.9%) missing valuesMissing
div2totalgtime has 1099417 (> 99.9%) missing valuesMissing
div2longestgtime has 1099417 (> 99.9%) missing valuesMissing
div2wheelsoff has 1099438 (> 99.9%) missing valuesMissing
div2tailnum has 1099438 (> 99.9%) missing valuesMissing
div3airport has 1099444 (100.0%) missing valuesMissing
div3airportid has 1099444 (100.0%) missing valuesMissing
div3airportseqid has 1099444 (100.0%) missing valuesMissing
div3wheelson has 1099444 (100.0%) missing valuesMissing
div3totalgtime has 1099444 (100.0%) missing valuesMissing
div3longestgtime has 1099444 (100.0%) missing valuesMissing
div3wheelsoff has 1099444 (100.0%) missing valuesMissing
div3tailnum has 1099444 (100.0%) missing valuesMissing
div4airport has 1099444 (100.0%) missing valuesMissing
div4airportid has 1099444 (100.0%) missing valuesMissing
div4airportseqid has 1099444 (100.0%) missing valuesMissing
div4wheelson has 1099444 (100.0%) missing valuesMissing
div4totalgtime has 1099444 (100.0%) missing valuesMissing
div4longestgtime has 1099444 (100.0%) missing valuesMissing
div4wheelsoff has 1099444 (100.0%) missing valuesMissing
div4tailnum has 1099444 (100.0%) missing valuesMissing
div5airport has 1099444 (100.0%) missing valuesMissing
div5airportid has 1099444 (100.0%) missing valuesMissing
div5airportseqid has 1099444 (100.0%) missing valuesMissing
div5wheelson has 1099444 (100.0%) missing valuesMissing
div5totalgtime has 1099444 (100.0%) missing valuesMissing
div5longestgtime has 1099444 (100.0%) missing valuesMissing
div5wheelsoff has 1099444 (100.0%) missing valuesMissing
div5tailnum has 1099444 (100.0%) missing valuesMissing
empty_col has 1099444 (100.0%) missing valuesMissing
diverted is highly skewed (γ1 = 22.77737356)Skewed
securitydelay is highly skewed (γ1 = 85.93091604)Skewed
divairportlandings is highly skewed (γ1 = 62.89861948)Skewed
div3airport is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3airportid is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3airportseqid is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3wheelson is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3totalgtime is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3longestgtime is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3wheelsoff is an unsupported type, check if it needs cleaning or further analysisUnsupported
div3tailnum is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4airport is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4airportid is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4airportseqid is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4wheelson is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4totalgtime is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4longestgtime is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4wheelsoff is an unsupported type, check if it needs cleaning or further analysisUnsupported
div4tailnum is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5airport is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5airportid is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5airportseqid is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5wheelson is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5totalgtime is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5longestgtime is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5wheelsoff is an unsupported type, check if it needs cleaning or further analysisUnsupported
div5tailnum is an unsupported type, check if it needs cleaning or further analysisUnsupported
empty_col is an unsupported type, check if it needs cleaning or further analysisUnsupported
depdelay has 59738 (5.4%) zerosZeros
depdelayminutes has 669227 (60.9%) zerosZeros
depdel15 has 871902 (79.3%) zerosZeros
departuredelaygroups has 262413 (23.9%) zerosZeros
arrdelay has 20607 (1.9%) zerosZeros
arrdelayminutes has 701533 (63.8%) zerosZeros
arrdel15 has 879761 (80.0%) zerosZeros
arrivaldelaygroups has 198835 (18.1%) zerosZeros
cancelled has 1082691 (98.5%) zerosZeros
diverted has 1097337 (99.8%) zerosZeros
carrierdelay has 70542 (6.4%) zerosZeros
weatherdelay has 193041 (17.6%) zerosZeros
nasdelay has 116711 (10.6%) zerosZeros
securitydelay has 199313 (18.1%) zerosZeros
lateaircraftdelay has 106887 (9.7%) zerosZeros
divairportlandings has 1097142 (99.8%) zerosZeros

Reproduction

Analysis started2023-12-06 17:31:10.287945
Analysis finished2023-12-06 17:31:27.008678
Duration16.72 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

year
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021
Minimum2021
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:27.235727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2021
5-th percentile2021
Q12021
median2021
Q32021
95-th percentile2021
Maximum2021
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2021
Median Absolute Deviation (MAD)0
Skewness0
Sum2221976324
Variance0
MonotonicityIncreasing
2023-12-06T17:31:27.370044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2021 1099444
100.0%
ValueCountFrequency (%)
2021 1099444
100.0%
ValueCountFrequency (%)
2021 1099444
100.0%

quarter
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum4
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:27.499254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum4
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean4
Median Absolute Deviation (MAD)0
Skewness0
Sum4397776
Variance0
MonotonicityIncreasing
2023-12-06T17:31:27.618817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
4 1099444
100.0%
ValueCountFrequency (%)
4 1099444
100.0%
ValueCountFrequency (%)
4 1099444
100.0%

month
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.50196736
Minimum11
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:27.752036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median12
Q312
95-th percentile12
Maximum12
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4999963569
Coefficient of variation (CV)0.04347050738
Kurtosis-1.999941709
Mean11.50196736
Median Absolute Deviation (MAD)0
Skewness-0.00786950384
Sum12645769
Variance0.2499963569
MonotonicityNot monotonic
2023-12-06T17:31:27.884728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
12 551885
50.2%
11 547559
49.8%
ValueCountFrequency (%)
11 547559
49.8%
12 551885
50.2%
ValueCountFrequency (%)
12 551885
50.2%
11 547559
49.8%

dayofmonth
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.74288731
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:28.035491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.803721563
Coefficient of variation (CV)0.5592189913
Kurtosis-1.194608969
Mean15.74288731
Median Absolute Deviation (MAD)8
Skewness0.000965916908
Sum17308423
Variance77.50551336
MonotonicityNot monotonic
2023-12-06T17:31:28.218857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
23 38651
 
3.5%
29 38525
 
3.5%
28 38475
 
3.5%
19 38073
 
3.5%
5 38003
 
3.5%
27 37784
 
3.4%
12 37721
 
3.4%
22 37573
 
3.4%
21 37383
 
3.4%
30 36889
 
3.4%
Other values (21) 720367
65.5%
ValueCountFrequency (%)
1 36584
3.3%
2 36001
3.3%
3 36736
3.3%
4 35141
3.2%
5 38003
3.5%
ValueCountFrequency (%)
31 15506
1.4%
30 36889
3.4%
29 38525
3.5%
28 38475
3.5%
27 37784
3.4%

dayofweek
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.897726487
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:28.351886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.99239076
Coefficient of variation (CV)0.5111674118
Kurtosis-1.21262101
Mean3.897726487
Median Absolute Deviation (MAD)2
Skewness0.06852645996
Sum4285332
Variance3.969620942
MonotonicityNot monotonic
2023-12-06T17:31:28.501887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 170567
15.5%
4 164793
15.0%
3 161871
14.7%
5 161358
14.7%
2 158191
14.4%
7 150824
13.7%
6 131840
12.0%
ValueCountFrequency (%)
1 170567
15.5%
2 158191
14.4%
3 161871
14.7%
4 164793
15.0%
5 161358
14.7%
ValueCountFrequency (%)
7 150824
13.7%
6 131840
12.0%
5 161358
14.7%
4 164793
15.0%
3 161871
14.7%
Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:28.851482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10994440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-08
2nd row2021-12-09
3rd row2021-12-10
4th row2021-12-11
5th row2021-12-12
ValueCountFrequency (%)
2021-11-28 20248
 
1.8%
2021-11-29 20054
 
1.8%
2021-11-24 19965
 
1.8%
2021-11-19 19556
 
1.8%
2021-11-04 19514
 
1.8%
2021-11-05 19471
 
1.8%
2021-11-18 19441
 
1.8%
2021-11-23 19396
 
1.8%
2021-11-11 19349
 
1.8%
2021-11-12 19304
 
1.8%
Other values (51) 903146
82.1%
2023-12-06T17:31:29.401061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3232672
29.4%
2 3222811
29.3%
- 2198888
20.0%
0 1532941
13.9%
3 162183
 
1.5%
9 112170
 
1.0%
8 111388
 
1.0%
7 109919
 
1.0%
4 105940
 
1.0%
6 104535
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8795552
80.0%
Dash Punctuation 2198888
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3232672
36.8%
2 3222811
36.6%
0 1532941
17.4%
3 162183
 
1.8%
9 112170
 
1.3%
8 111388
 
1.3%
7 109919
 
1.2%
4 105940
 
1.2%
6 104535
 
1.2%
5 100993
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 2198888
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10994440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3232672
29.4%
2 3222811
29.3%
- 2198888
20.0%
0 1532941
13.9%
3 162183
 
1.5%
9 112170
 
1.0%
8 111388
 
1.0%
7 109919
 
1.0%
4 105940
 
1.0%
6 104535
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10994440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3232672
29.4%
2 3222811
29.3%
- 2198888
20.0%
0 1532941
13.9%
3 162183
 
1.5%
9 112170
 
1.0%
8 111388
 
1.0%
7 109919
 
1.0%
4 105940
 
1.0%
6 104535
 
1.0%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:29.614724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2198888
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowOH
3rd rowOH
4th rowOH
5th rowOH
ValueCountFrequency (%)
wn 194737
17.7%
aa 144732
13.2%
dl 138898
12.6%
oo 132218
12.0%
ua 96753
8.8%
yx 55141
 
5.0%
mq 45171
 
4.1%
9e 43025
 
3.9%
b6 40806
 
3.7%
oh 40262
 
3.7%
Other values (7) 167701
15.3%
2023-12-06T17:31:29.968073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 431950
19.6%
O 304698
13.9%
N 230163
10.5%
W 194737
8.9%
D 138898
 
6.3%
L 138898
 
6.3%
U 96753
 
4.4%
Y 81297
 
3.7%
X 70226
 
3.2%
9 68988
 
3.1%
Other values (12) 442280
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2069756
94.1%
Decimal Number 129132
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 431950
20.9%
O 304698
14.7%
N 230163
11.1%
W 194737
9.4%
D 138898
 
6.7%
L 138898
 
6.7%
U 96753
 
4.7%
Y 81297
 
3.9%
X 70226
 
3.4%
Q 60256
 
2.9%
Other values (9) 321880
15.6%
Decimal Number
ValueCountFrequency (%)
9 68988
53.4%
6 40806
31.6%
4 19338
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2069756
94.1%
Common 129132
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 431950
20.9%
O 304698
14.7%
N 230163
11.1%
W 194737
9.4%
D 138898
 
6.7%
L 138898
 
6.7%
U 96753
 
4.7%
Y 81297
 
3.9%
X 70226
 
3.4%
Q 60256
 
2.9%
Other values (9) 321880
15.6%
Common
ValueCountFrequency (%)
9 68988
53.4%
6 40806
31.6%
4 19338
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2198888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 431950
19.6%
O 304698
13.9%
N 230163
10.5%
W 194737
8.9%
D 138898
 
6.3%
L 138898
 
6.3%
U 96753
 
4.4%
Y 81297
 
3.7%
X 70226
 
3.2%
9 68988
 
3.1%
Other values (12) 442280
20.1%

dot_id_reporting_airline
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19987.11542
Minimum19393
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:30.138427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19393
Q119790
median19977
Q320368
95-th percentile20452
Maximum20452
Range1059
Interquartile range (IQR)578

Descriptive statistics

Standard deviation372.9746127
Coefficient of variation (CV)0.01866075243
Kurtosis-1.279674603
Mean19987.11542
Median Absolute Deviation (MAD)327
Skewness-0.2581749927
Sum2.197471413 × 1010
Variance139110.0617
MonotonicityNot monotonic
2023-12-06T17:31:30.302208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
19393 194737
17.7%
19805 144732
13.2%
19790 138898
12.6%
20304 132218
12.0%
19977 96753
8.8%
20452 55141
 
5.0%
20398 45171
 
4.1%
20363 43025
 
3.9%
20409 40806
 
3.7%
20397 40262
 
3.7%
Other values (7) 167701
15.3%
ValueCountFrequency (%)
19393 194737
17.7%
19687 15085
 
1.4%
19690 11955
 
1.1%
19790 138898
12.6%
19805 144732
13.2%
ValueCountFrequency (%)
20452 55141
5.0%
20436 25963
2.4%
20416 35426
3.2%
20409 40806
3.7%
20398 45171
4.1%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:30.501300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2198888
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowOH
3rd rowOH
4th rowOH
5th rowOH
ValueCountFrequency (%)
wn 194737
17.7%
aa 144732
13.2%
dl 138898
12.6%
oo 132218
12.0%
ua 96753
8.8%
yx 55141
 
5.0%
mq 45171
 
4.1%
9e 43025
 
3.9%
b6 40806
 
3.7%
oh 40262
 
3.7%
Other values (7) 167701
15.3%
2023-12-06T17:31:30.868634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 431950
19.6%
O 304698
13.9%
N 230163
10.5%
W 194737
8.9%
D 138898
 
6.3%
L 138898
 
6.3%
U 96753
 
4.4%
Y 81297
 
3.7%
X 70226
 
3.2%
9 68988
 
3.1%
Other values (12) 442280
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2069756
94.1%
Decimal Number 129132
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 431950
20.9%
O 304698
14.7%
N 230163
11.1%
W 194737
9.4%
D 138898
 
6.7%
L 138898
 
6.7%
U 96753
 
4.7%
Y 81297
 
3.9%
X 70226
 
3.4%
Q 60256
 
2.9%
Other values (9) 321880
15.6%
Decimal Number
ValueCountFrequency (%)
9 68988
53.4%
6 40806
31.6%
4 19338
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2069756
94.1%
Common 129132
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 431950
20.9%
O 304698
14.7%
N 230163
11.1%
W 194737
9.4%
D 138898
 
6.7%
L 138898
 
6.7%
U 96753
 
4.7%
Y 81297
 
3.9%
X 70226
 
3.4%
Q 60256
 
2.9%
Other values (9) 321880
15.6%
Common
ValueCountFrequency (%)
9 68988
53.4%
6 40806
31.6%
4 19338
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2198888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 431950
19.6%
O 304698
13.9%
N 230163
10.5%
W 194737
8.9%
D 138898
 
6.3%
L 138898
 
6.3%
U 96753
 
4.4%
Y 81297
 
3.7%
X 70226
 
3.2%
9 68988
 
3.1%
Other values (12) 442280
20.1%
Distinct5379
Distinct (%)0.5%
Missing3255
Missing (%)0.3%
Memory size8.4 MiB
2023-12-06T17:31:31.317504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.980525256
Min length5

Characters and Unicode

Total characters6555786
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowN522AE
2nd rowN529EA
3rd rowN500AE
4th rowN531EG
5th rowN500AE
ValueCountFrequency (%)
n480ha 582
 
0.1%
n483ha 568
 
0.1%
n488ha 567
 
0.1%
n493ha 520
 
< 0.1%
n484ha 517
 
< 0.1%
n487ha 498
 
< 0.1%
n476ha 496
 
< 0.1%
n478ha 491
 
< 0.1%
n479ha 481
 
< 0.1%
n486ha 477
 
< 0.1%
Other values (5369) 1090992
99.5%
2023-12-06T17:31:31.933882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1475062
22.5%
8 429536
 
6.6%
3 407293
 
6.2%
2 406136
 
6.2%
7 375882
 
5.7%
6 352948
 
5.4%
4 340018
 
5.2%
1 339243
 
5.2%
5 337269
 
5.1%
9 334622
 
5.1%
Other values (24) 1757777
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3576566
54.6%
Uppercase Letter 2979220
45.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1475062
49.5%
A 226148
 
7.6%
W 177350
 
6.0%
S 159775
 
5.4%
U 94151
 
3.2%
D 88254
 
3.0%
Y 78136
 
2.6%
K 74883
 
2.5%
J 74013
 
2.5%
Q 64253
 
2.2%
Other values (14) 467195
 
15.7%
Decimal Number
ValueCountFrequency (%)
8 429536
12.0%
3 407293
11.4%
2 406136
11.4%
7 375882
10.5%
6 352948
9.9%
4 340018
9.5%
1 339243
9.5%
5 337269
9.4%
9 334622
9.4%
0 253619
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3576566
54.6%
Latin 2979220
45.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1475062
49.5%
A 226148
 
7.6%
W 177350
 
6.0%
S 159775
 
5.4%
U 94151
 
3.2%
D 88254
 
3.0%
Y 78136
 
2.6%
K 74883
 
2.5%
J 74013
 
2.5%
Q 64253
 
2.2%
Other values (14) 467195
 
15.7%
Common
ValueCountFrequency (%)
8 429536
12.0%
3 407293
11.4%
2 406136
11.4%
7 375882
10.5%
6 352948
9.9%
4 340018
9.5%
1 339243
9.5%
5 337269
9.4%
9 334622
9.4%
0 253619
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6555786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1475062
22.5%
8 429536
 
6.6%
3 407293
 
6.2%
2 406136
 
6.2%
7 375882
 
5.7%
6 352948
 
5.4%
4 340018
 
5.2%
1 339243
 
5.2%
5 337269
 
5.1%
9 334622
 
5.1%
Other values (24) 1757777
26.8%
Distinct6416
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2678.969059
Minimum1
Maximum8802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:32.150794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile304
Q11175
median2339
Q34152
95-th percentile5731
Maximum8802
Range8801
Interquartile range (IQR)2977

Descriptive statistics

Standard deviation1781.394938
Coefficient of variation (CV)0.6649553983
Kurtosis-1.081298192
Mean2678.969059
Median Absolute Deviation (MAD)1385
Skewness0.3788250665
Sum2945376458
Variance3173367.924
MonotonicityNot monotonic
2023-12-06T17:31:32.350677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
371 505
 
< 0.1%
342 484
 
< 0.1%
1103 483
 
< 0.1%
2081 474
 
< 0.1%
539 462
 
< 0.1%
1274 459
 
< 0.1%
340 457
 
< 0.1%
698 455
 
< 0.1%
778 450
 
< 0.1%
303 448
 
< 0.1%
Other values (6406) 1094767
99.6%
ValueCountFrequency (%)
1 320
< 0.1%
2 330
< 0.1%
3 309
< 0.1%
4 247
< 0.1%
5 182
< 0.1%
ValueCountFrequency (%)
8802 1
< 0.1%
8801 1
< 0.1%
8799 1
< 0.1%
8796 1
< 0.1%
8794 1
< 0.1%

originairportid
Real number (ℝ)

Distinct363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12658.35478
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:32.534390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1521.734078
Coefficient of variation (CV)0.1202157867
Kurtosis-1.29201414
Mean12658.35478
Median Absolute Deviation (MAD)1466
Skewness0.1036694406
Sum1.391715221 × 1010
Variance2315674.603
MonotonicityNot monotonic
2023-12-06T17:31:32.751449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 52884
 
4.8%
11298 46963
 
4.3%
13930 45283
 
4.1%
11292 43601
 
4.0%
11057 33232
 
3.0%
12892 32373
 
2.9%
14107 28912
 
2.6%
14747 26649
 
2.4%
12953 26248
 
2.4%
12889 25842
 
2.4%
Other values (353) 737457
67.1%
ValueCountFrequency (%)
10135 736
 
0.1%
10136 337
 
< 0.1%
10140 3214
0.3%
10141 132
 
< 0.1%
10146 156
 
< 0.1%
ValueCountFrequency (%)
16869 242
 
< 0.1%
16218 305
 
< 0.1%
16101 117
 
< 0.1%
15991 118
 
< 0.1%
15919 1695
0.2%

originairportseqid
Real number (ℝ)

Distinct363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1265839.322
Minimum1013506
Maximum1686901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:32.950697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1013506
5-th percentile1039707
Q11129202
median1288903
Q31402702
95-th percentile1489302
Maximum1686901
Range673395
Interquartile range (IQR)273500

Descriptive statistics

Standard deviation152173.1649
Coefficient of variation (CV)0.1202152297
Kurtosis-1.292019765
Mean1265839.322
Median Absolute Deviation (MAD)146596
Skewness0.1036709535
Sum1.391719447 × 1012
Variance2.315667211 × 1010
MonotonicityNot monotonic
2023-12-06T17:31:33.171080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039707 52884
 
4.8%
1129806 46963
 
4.3%
1393007 45283
 
4.1%
1129202 43601
 
4.0%
1105703 33232
 
3.0%
1289208 32373
 
2.9%
1410702 28912
 
2.6%
1474703 26649
 
2.4%
1295304 26248
 
2.4%
1288903 25842
 
2.4%
Other values (353) 737457
67.1%
ValueCountFrequency (%)
1013506 736
 
0.1%
1013603 337
 
< 0.1%
1014005 3214
0.3%
1014106 132
 
< 0.1%
1014602 156
 
< 0.1%
ValueCountFrequency (%)
1686901 242
 
< 0.1%
1621802 305
 
< 0.1%
1610102 117
 
< 0.1%
1599102 118
 
< 0.1%
1591904 1695
0.2%

origincitymarketid
Real number (ℝ)

Distinct339
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31747.74808
Minimum30070
Maximum36101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:33.383986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30194
Q130666
median31453
Q332467
95-th percentile34570
Maximum36101
Range6031
Interquartile range (IQR)1801

Descriptive statistics

Standard deviation1318.265747
Coefficient of variation (CV)0.04152312612
Kurtosis-0.2488787348
Mean31747.74808
Median Absolute Deviation (MAD)987
Skewness0.8274902258
Sum3.490487114 × 1010
Variance1737824.581
MonotonicityNot monotonic
2023-12-06T17:31:33.583563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31703 71060
 
6.5%
30194 57584
 
5.2%
30977 55783
 
5.1%
30397 52884
 
4.8%
32575 50613
 
4.6%
30852 45806
 
4.2%
30325 43601
 
4.0%
32467 34416
 
3.1%
32457 34007
 
3.1%
31057 33232
 
3.0%
Other values (329) 620458
56.4%
ValueCountFrequency (%)
30070 135
 
< 0.1%
30073 63
 
< 0.1%
30107 59
 
< 0.1%
30113 117
 
< 0.1%
30135 736
0.1%
ValueCountFrequency (%)
36101 117
< 0.1%
35991 118
< 0.1%
35841 118
< 0.1%
35582 109
< 0.1%
35569 105
< 0.1%

origin
Text

Distinct363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:34.197335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3298332
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWN
2nd rowEWN
3rd rowEWN
4th rowEWN
5th rowEWN
ValueCountFrequency (%)
atl 52884
 
4.8%
dfw 46963
 
4.3%
ord 45283
 
4.1%
den 43601
 
4.0%
clt 33232
 
3.0%
lax 32373
 
2.9%
phx 28912
 
2.6%
sea 26649
 
2.4%
lga 26248
 
2.4%
las 25842
 
2.4%
Other values (353) 737457
67.1%
2023-12-06T17:31:34.967248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 375739
 
11.4%
L 300599
 
9.1%
S 273935
 
8.3%
D 261611
 
7.9%
T 177811
 
5.4%
C 166178
 
5.0%
O 166126
 
5.0%
M 146158
 
4.4%
F 136242
 
4.1%
W 130419
 
4.0%
Other values (16) 1163514
35.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3298332
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 375739
 
11.4%
L 300599
 
9.1%
S 273935
 
8.3%
D 261611
 
7.9%
T 177811
 
5.4%
C 166178
 
5.0%
O 166126
 
5.0%
M 146158
 
4.4%
F 136242
 
4.1%
W 130419
 
4.0%
Other values (16) 1163514
35.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3298332
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 375739
 
11.4%
L 300599
 
9.1%
S 273935
 
8.3%
D 261611
 
7.9%
T 177811
 
5.4%
C 166178
 
5.0%
O 166126
 
5.0%
M 146158
 
4.4%
F 136242
 
4.1%
W 130419
 
4.0%
Other values (16) 1163514
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3298332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 375739
 
11.4%
L 300599
 
9.1%
S 273935
 
8.3%
D 261611
 
7.9%
T 177811
 
5.4%
C 166178
 
5.0%
O 166126
 
5.0%
M 146158
 
4.4%
F 136242
 
4.1%
W 130419
 
4.0%
Other values (16) 1163514
35.3%
Distinct357
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:35.430553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.08778619
Min length8

Characters and Unicode

Total characters14389288
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Bern/Morehead/Beaufort, NC
2nd rowNew Bern/Morehead/Beaufort, NC
3rd rowNew Bern/Morehead/Beaufort, NC
4th rowNew Bern/Morehead/Beaufort, NC
5th rowNew Bern/Morehead/Beaufort, NC
ValueCountFrequency (%)
tx 118200
 
4.6%
ca 113391
 
4.4%
fl 104646
 
4.1%
ny 58628
 
2.3%
il 58320
 
2.3%
ga 57230
 
2.2%
chicago 55783
 
2.2%
new 54263
 
2.1%
atlanta 52884
 
2.1%
san 50375
 
2.0%
Other values (433) 1836112
71.7%
2023-12-06T17:31:36.132848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1460388
 
10.1%
, 1099444
 
7.6%
a 1089724
 
7.6%
o 799843
 
5.6%
e 760365
 
5.3%
t 700203
 
4.9%
n 698092
 
4.9%
l 630399
 
4.4%
i 550721
 
3.8%
r 526330
 
3.7%
Other values (46) 6073779
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7987524
55.5%
Uppercase Letter 3745325
26.0%
Space Separator 1460388
 
10.1%
Other Punctuation 1195709
 
8.3%
Dash Punctuation 342
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1089724
13.6%
o 799843
10.0%
e 760365
9.5%
t 700203
8.8%
n 698092
8.7%
l 630399
7.9%
i 550721
 
6.9%
r 526330
 
6.6%
s 501395
 
6.3%
h 338063
 
4.2%
Other values (16) 1392389
17.4%
Uppercase Letter
ValueCountFrequency (%)
A 446194
 
11.9%
C 414919
 
11.1%
N 316855
 
8.5%
L 295654
 
7.9%
D 207448
 
5.5%
F 205776
 
5.5%
T 189154
 
5.1%
M 188320
 
5.0%
S 165028
 
4.4%
O 159920
 
4.3%
Other values (15) 1156057
30.9%
Other Punctuation
ValueCountFrequency (%)
, 1099444
91.9%
/ 85043
 
7.1%
. 11222
 
0.9%
Space Separator
ValueCountFrequency (%)
1460388
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11732849
81.5%
Common 2656439
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1089724
 
9.3%
o 799843
 
6.8%
e 760365
 
6.5%
t 700203
 
6.0%
n 698092
 
5.9%
l 630399
 
5.4%
i 550721
 
4.7%
r 526330
 
4.5%
s 501395
 
4.3%
A 446194
 
3.8%
Other values (41) 5029583
42.9%
Common
ValueCountFrequency (%)
1460388
55.0%
, 1099444
41.4%
/ 85043
 
3.2%
. 11222
 
0.4%
- 342
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14389288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1460388
 
10.1%
, 1099444
 
7.6%
a 1089724
 
7.6%
o 799843
 
5.6%
e 760365
 
5.3%
t 700203
 
4.9%
n 698092
 
4.9%
l 630399
 
4.4%
i 550721
 
3.8%
r 526330
 
3.7%
Other values (46) 6073779
42.2%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:36.434168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2198888
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowNC
3rd rowNC
4th rowNC
5th rowNC
ValueCountFrequency (%)
tx 118200
 
10.8%
ca 113391
 
10.3%
fl 104646
 
9.5%
ny 58628
 
5.3%
il 58320
 
5.3%
ga 57230
 
5.2%
co 49351
 
4.5%
nc 47360
 
4.3%
va 39913
 
3.6%
az 33626
 
3.1%
Other values (43) 418779
38.1%
2023-12-06T17:31:36.901179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 349110
15.9%
C 222930
10.1%
N 222183
10.1%
L 177925
 
8.1%
T 168065
 
7.6%
I 137912
 
6.3%
X 118200
 
5.4%
M 113546
 
5.2%
F 104646
 
4.8%
O 101979
 
4.6%
Other values (14) 482392
21.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2198888
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 349110
15.9%
C 222930
10.1%
N 222183
10.1%
L 177925
 
8.1%
T 168065
 
7.6%
I 137912
 
6.3%
X 118200
 
5.4%
M 113546
 
5.2%
F 104646
 
4.8%
O 101979
 
4.6%
Other values (14) 482392
21.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 2198888
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 349110
15.9%
C 222930
10.1%
N 222183
10.1%
L 177925
 
8.1%
T 168065
 
7.6%
I 137912
 
6.3%
X 118200
 
5.4%
M 113546
 
5.2%
F 104646
 
4.8%
O 101979
 
4.6%
Other values (14) 482392
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2198888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 349110
15.9%
C 222930
10.1%
N 222183
10.1%
L 177925
 
8.1%
T 168065
 
7.6%
I 137912
 
6.3%
X 118200
 
5.4%
M 113546
 
5.2%
F 104646
 
4.8%
O 101979
 
4.6%
Other values (14) 482392
21.9%

originstatefips
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.11016296
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:37.083025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median26
Q342
95-th percentile51
Maximum78
Range77
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.72663708
Coefficient of variation (CV)0.6169876998
Kurtosis-1.218489304
Mean27.11016296
Median Absolute Deviation (MAD)14
Skewness0.2126702396
Sum29806106
Variance279.7803881
MonotonicityNot monotonic
2023-12-06T17:31:37.282677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 118200
 
10.8%
6 113391
 
10.3%
12 104646
 
9.5%
36 58628
 
5.3%
17 58320
 
5.3%
13 57230
 
5.2%
8 49351
 
4.5%
37 47360
 
4.3%
51 39913
 
3.6%
4 33626
 
3.1%
Other values (43) 418779
38.1%
ValueCountFrequency (%)
1 4582
 
0.4%
2 5888
 
0.5%
4 33626
 
3.1%
5 3708
 
0.3%
6 113391
10.3%
ValueCountFrequency (%)
78 1210
 
0.1%
75 186
 
< 0.1%
72 5313
0.5%
56 1709
 
0.2%
55 8715
0.8%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:37.599086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.127619051
Min length4

Characters and Unicode

Total characters8935862
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Carolina
2nd rowNorth Carolina
3rd rowNorth Carolina
4th rowNorth Carolina
5th rowNorth Carolina
ValueCountFrequency (%)
texas 118200
 
9.4%
california 113391
 
9.0%
florida 104646
 
8.3%
new 86328
 
6.9%
york 58628
 
4.7%
illinois 58320
 
4.6%
georgia 57230
 
4.5%
carolina 56480
 
4.5%
north 50287
 
4.0%
colorado 49351
 
3.9%
Other values (52) 506032
40.2%
2023-12-06T17:31:38.070779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1191970
13.3%
i 1019870
 
11.4%
o 860208
 
9.6%
n 666526
 
7.5%
r 649288
 
7.3%
e 535341
 
6.0%
s 502637
 
5.6%
l 497667
 
5.6%
C 222930
 
2.5%
d 216870
 
2.4%
Other values (37) 2572555
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7513518
84.1%
Uppercase Letter 1260103
 
14.1%
Space Separator 159449
 
1.8%
Other Punctuation 2792
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1191970
15.9%
i 1019870
13.6%
o 860208
11.4%
n 666526
8.9%
r 649288
8.6%
e 535341
7.1%
s 502637
6.7%
l 497667
6.6%
d 216870
 
2.9%
t 211103
 
2.8%
Other values (14) 1162038
15.5%
Uppercase Letter
ValueCountFrequency (%)
C 222930
17.7%
N 170117
13.5%
T 139565
11.1%
M 113546
9.0%
F 104646
8.3%
I 80814
 
6.4%
Y 58628
 
4.7%
G 57230
 
4.5%
A 47804
 
3.8%
W 43059
 
3.4%
Other values (11) 221764
17.6%
Space Separator
ValueCountFrequency (%)
159449
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8773621
98.2%
Common 162241
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1191970
13.6%
i 1019870
11.6%
o 860208
 
9.8%
n 666526
 
7.6%
r 649288
 
7.4%
e 535341
 
6.1%
s 502637
 
5.7%
l 497667
 
5.7%
C 222930
 
2.5%
d 216870
 
2.5%
Other values (35) 2410314
27.5%
Common
ValueCountFrequency (%)
159449
98.3%
. 2792
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8935862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1191970
13.3%
i 1019870
 
11.4%
o 860208
 
9.6%
n 666526
 
7.5%
r 649288
 
7.3%
e 535341
 
6.0%
s 502637
 
5.6%
l 497667
 
5.6%
C 222930
 
2.5%
d 216870
 
2.4%
Other values (37) 2572555
28.8%

originwac
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.46769913
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:38.414433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median44
Q381
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)47

Descriptive statistics

Standard deviation26.54386067
Coefficient of variation (CV)0.4873321453
Kurtosis-1.306737865
Mean54.46769913
Median Absolute Deviation (MAD)22
Skewness-0.008870534065
Sum59884185
Variance704.5765391
MonotonicityNot monotonic
2023-12-06T17:31:38.611489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 118200
 
10.8%
91 113391
 
10.3%
33 104646
 
9.5%
22 58628
 
5.3%
41 58320
 
5.3%
34 57230
 
5.2%
82 49351
 
4.5%
36 47360
 
4.3%
38 39913
 
3.6%
81 33626
 
3.1%
Other values (43) 418779
38.1%
ValueCountFrequency (%)
1 5888
 
0.5%
2 20365
1.9%
3 5313
 
0.5%
4 1210
 
0.1%
5 186
 
< 0.1%
ValueCountFrequency (%)
93 31891
 
2.9%
92 13324
 
1.2%
91 113391
10.3%
88 1709
 
0.2%
87 19680
 
1.8%

destairportid
Real number (ℝ)

Distinct363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12658.50633
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:38.815648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1521.730415
Coefficient of variation (CV)0.1202140581
Kurtosis-1.292070345
Mean12658.50633
Median Absolute Deviation (MAD)1466
Skewness0.1034632258
Sum1.391731883 × 1010
Variance2315663.456
MonotonicityNot monotonic
2023-12-06T17:31:39.015934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 52890
 
4.8%
11298 46933
 
4.3%
13930 45291
 
4.1%
11292 43605
 
4.0%
11057 33232
 
3.0%
12892 32377
 
2.9%
14107 28913
 
2.6%
14747 26647
 
2.4%
12953 26242
 
2.4%
12889 25847
 
2.4%
Other values (353) 737467
67.1%
ValueCountFrequency (%)
10135 737
 
0.1%
10136 337
 
< 0.1%
10140 3214
0.3%
10141 132
 
< 0.1%
10146 156
 
< 0.1%
ValueCountFrequency (%)
16869 242
 
< 0.1%
16218 305
 
< 0.1%
16101 117
 
< 0.1%
15991 118
 
< 0.1%
15919 1692
0.2%

destairportseqid
Real number (ℝ)

Distinct363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1265854.477
Minimum1013506
Maximum1686901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:39.215915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1013506
5-th percentile1039707
Q11129202
median1288903
Q31402702
95-th percentile1489302
Maximum1686901
Range673395
Interquartile range (IQR)273500

Descriptive statistics

Standard deviation152172.7985
Coefficient of variation (CV)0.1202135011
Kurtosis-1.292075971
Mean1265854.477
Median Absolute Deviation (MAD)146596
Skewness0.1034647384
Sum1.391736109 × 1012
Variance2.31565606 × 1010
MonotonicityNot monotonic
2023-12-06T17:31:39.431895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039707 52890
 
4.8%
1129806 46933
 
4.3%
1393007 45291
 
4.1%
1129202 43605
 
4.0%
1105703 33232
 
3.0%
1289208 32377
 
2.9%
1410702 28913
 
2.6%
1474703 26647
 
2.4%
1295304 26242
 
2.4%
1288903 25847
 
2.4%
Other values (353) 737467
67.1%
ValueCountFrequency (%)
1013506 737
 
0.1%
1013603 337
 
< 0.1%
1014005 3214
0.3%
1014106 132
 
< 0.1%
1014602 156
 
< 0.1%
ValueCountFrequency (%)
1686901 242
 
< 0.1%
1621802 305
 
< 0.1%
1610102 117
 
< 0.1%
1599102 118
 
< 0.1%
1591904 1692
0.2%

destcitymarketid
Real number (ℝ)

Distinct339
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31747.87393
Minimum30070
Maximum36101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:39.632116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30194
Q130666
median31453
Q332467
95-th percentile34570
Maximum36101
Range6031
Interquartile range (IQR)1801

Descriptive statistics

Standard deviation1318.297629
Coefficient of variation (CV)0.04152396572
Kurtosis-0.249113869
Mean31747.87393
Median Absolute Deviation (MAD)987
Skewness0.8273786192
Sum3.49050095 × 1010
Variance1737908.638
MonotonicityNot monotonic
2023-12-06T17:31:39.832296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31703 71046
 
6.5%
30194 57552
 
5.2%
30977 55790
 
5.1%
30397 52890
 
4.8%
32575 50615
 
4.6%
30852 45791
 
4.2%
30325 43605
 
4.0%
32467 34454
 
3.1%
32457 34008
 
3.1%
31057 33232
 
3.0%
Other values (329) 620461
56.4%
ValueCountFrequency (%)
30070 135
 
< 0.1%
30073 63
 
< 0.1%
30107 59
 
< 0.1%
30113 117
 
< 0.1%
30135 737
0.1%
ValueCountFrequency (%)
36101 117
< 0.1%
35991 118
< 0.1%
35841 118
< 0.1%
35582 109
< 0.1%
35569 105
< 0.1%

dest
Text

Distinct363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:40.414617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3298332
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLT
2nd rowCLT
3rd rowCLT
4th rowCLT
5th rowCLT
ValueCountFrequency (%)
atl 52890
 
4.8%
dfw 46933
 
4.3%
ord 45291
 
4.1%
den 43605
 
4.0%
clt 33232
 
3.0%
lax 32377
 
2.9%
phx 28913
 
2.6%
sea 26647
 
2.4%
lga 26242
 
2.4%
las 25847
 
2.4%
Other values (353) 737467
67.1%
2023-12-06T17:31:41.165209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 375739
 
11.4%
L 300615
 
9.1%
S 273939
 
8.3%
D 261594
 
7.9%
T 177817
 
5.4%
C 166169
 
5.0%
O 166131
 
5.0%
M 146180
 
4.4%
F 136220
 
4.1%
W 130386
 
4.0%
Other values (16) 1163542
35.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3298332
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 375739
 
11.4%
L 300615
 
9.1%
S 273939
 
8.3%
D 261594
 
7.9%
T 177817
 
5.4%
C 166169
 
5.0%
O 166131
 
5.0%
M 146180
 
4.4%
F 136220
 
4.1%
W 130386
 
4.0%
Other values (16) 1163542
35.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3298332
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 375739
 
11.4%
L 300615
 
9.1%
S 273939
 
8.3%
D 261594
 
7.9%
T 177817
 
5.4%
C 166169
 
5.0%
O 166131
 
5.0%
M 146180
 
4.4%
F 136220
 
4.1%
W 130386
 
4.0%
Other values (16) 1163542
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3298332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 375739
 
11.4%
L 300615
 
9.1%
S 273939
 
8.3%
D 261594
 
7.9%
T 177817
 
5.4%
C 166169
 
5.0%
O 166131
 
5.0%
M 146180
 
4.4%
F 136220
 
4.1%
W 130386
 
4.0%
Other values (16) 1163542
35.3%
Distinct357
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:41.580484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.08761156
Min length8

Characters and Unicode

Total characters14389096
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCharlotte, NC
2nd rowCharlotte, NC
3rd rowCharlotte, NC
4th rowCharlotte, NC
5th rowCharlotte, NC
ValueCountFrequency (%)
tx 118133
 
4.6%
ca 113390
 
4.4%
fl 104736
 
4.1%
ny 58634
 
2.3%
il 58323
 
2.3%
ga 57235
 
2.2%
chicago 55790
 
2.2%
new 54274
 
2.1%
atlanta 52890
 
2.1%
san 50375
 
2.0%
Other values (433) 1836061
71.7%
2023-12-06T17:31:42.231598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1460397
 
10.1%
, 1099444
 
7.6%
a 1089740
 
7.6%
o 799745
 
5.6%
e 760442
 
5.3%
t 700110
 
4.9%
n 698063
 
4.9%
l 630359
 
4.4%
i 550738
 
3.8%
r 526316
 
3.7%
Other values (46) 6073742
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7987361
55.5%
Uppercase Letter 3745309
26.0%
Space Separator 1460397
 
10.1%
Other Punctuation 1195687
 
8.3%
Dash Punctuation 342
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1089740
13.6%
o 799745
10.0%
e 760442
9.5%
t 700110
8.8%
n 698063
8.7%
l 630359
7.9%
i 550738
 
6.9%
r 526316
 
6.6%
s 501356
 
6.3%
h 338031
 
4.2%
Other values (16) 1392461
17.4%
Uppercase Letter
ValueCountFrequency (%)
A 446162
 
11.9%
C 414894
 
11.1%
N 316856
 
8.5%
L 295769
 
7.9%
D 207409
 
5.5%
F 205848
 
5.5%
T 189100
 
5.0%
M 188355
 
5.0%
S 165014
 
4.4%
O 159938
 
4.3%
Other values (15) 1155964
30.9%
Other Punctuation
ValueCountFrequency (%)
, 1099444
92.0%
/ 85020
 
7.1%
. 11223
 
0.9%
Space Separator
ValueCountFrequency (%)
1460397
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11732670
81.5%
Common 2656426
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1089740
 
9.3%
o 799745
 
6.8%
e 760442
 
6.5%
t 700110
 
6.0%
n 698063
 
5.9%
l 630359
 
5.4%
i 550738
 
4.7%
r 526316
 
4.5%
s 501356
 
4.3%
A 446162
 
3.8%
Other values (41) 5029639
42.9%
Common
ValueCountFrequency (%)
1460397
55.0%
, 1099444
41.4%
/ 85020
 
3.2%
. 11223
 
0.4%
- 342
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14389096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1460397
 
10.1%
, 1099444
 
7.6%
a 1089740
 
7.6%
o 799745
 
5.6%
e 760442
 
5.3%
t 700110
 
4.9%
n 698063
 
4.9%
l 630359
 
4.4%
i 550738
 
3.8%
r 526316
 
3.7%
Other values (46) 6073742
42.2%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:42.497299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2198888
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowNC
3rd rowNC
4th rowNC
5th rowNC
ValueCountFrequency (%)
tx 118133
 
10.7%
ca 113390
 
10.3%
fl 104736
 
9.5%
ny 58634
 
5.3%
il 58323
 
5.3%
ga 57235
 
5.2%
co 49361
 
4.5%
nc 47366
 
4.3%
va 39892
 
3.6%
az 33629
 
3.1%
Other values (43) 418745
38.1%
2023-12-06T17:31:42.947872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 349076
15.9%
C 222934
10.1%
N 222192
10.1%
L 178018
 
8.1%
T 168005
 
7.6%
I 137922
 
6.3%
X 118133
 
5.4%
M 113544
 
5.2%
F 104736
 
4.8%
O 101991
 
4.6%
Other values (14) 482337
21.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2198888
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 349076
15.9%
C 222934
10.1%
N 222192
10.1%
L 178018
 
8.1%
T 168005
 
7.6%
I 137922
 
6.3%
X 118133
 
5.4%
M 113544
 
5.2%
F 104736
 
4.8%
O 101991
 
4.6%
Other values (14) 482337
21.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 2198888
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 349076
15.9%
C 222934
10.1%
N 222192
10.1%
L 178018
 
8.1%
T 168005
 
7.6%
I 137922
 
6.3%
X 118133
 
5.4%
M 113544
 
5.2%
F 104736
 
4.8%
O 101991
 
4.6%
Other values (14) 482337
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2198888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 349076
15.9%
C 222934
10.1%
N 222192
10.1%
L 178018
 
8.1%
T 168005
 
7.6%
I 137922
 
6.3%
X 118133
 
5.4%
M 113544
 
5.2%
F 104736
 
4.8%
O 101991
 
4.6%
Other values (14) 482337
21.9%

deststatefips
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.10708049
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:43.130934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median26
Q342
95-th percentile51
Maximum78
Range77
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.72610084
Coefficient of variation (CV)0.6170380778
Kurtosis-1.2182316
Mean27.10708049
Median Absolute Deviation (MAD)14
Skewness0.2130336157
Sum29802717
Variance279.7624493
MonotonicityNot monotonic
2023-12-06T17:31:43.347913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 118133
 
10.7%
6 113390
 
10.3%
12 104736
 
9.5%
36 58634
 
5.3%
17 58323
 
5.3%
13 57235
 
5.2%
8 49361
 
4.5%
37 47366
 
4.3%
51 39892
 
3.6%
4 33629
 
3.1%
Other values (43) 418745
38.1%
ValueCountFrequency (%)
1 4579
 
0.4%
2 5887
 
0.5%
4 33629
 
3.1%
5 3703
 
0.3%
6 113390
10.3%
ValueCountFrequency (%)
78 1208
 
0.1%
75 186
 
< 0.1%
72 5318
0.5%
56 1711
 
0.2%
55 8713
0.8%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:43.712140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.127544468
Min length4

Characters and Unicode

Total characters8935780
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Carolina
2nd rowNorth Carolina
3rd rowNorth Carolina
4th rowNorth Carolina
5th rowNorth Carolina
ValueCountFrequency (%)
texas 118133
 
9.4%
california 113390
 
9.0%
florida 104736
 
8.3%
new 86311
 
6.9%
york 58634
 
4.7%
illinois 58323
 
4.6%
georgia 57235
 
4.5%
carolina 56476
 
4.5%
north 50293
 
4.0%
colorado 49361
 
3.9%
Other values (52) 505977
40.2%
2023-12-06T17:31:44.184758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1191958
13.3%
i 1019910
 
11.4%
o 860340
 
9.6%
n 666504
 
7.5%
r 649356
 
7.3%
e 535220
 
6.0%
s 502495
 
5.6%
l 497759
 
5.6%
C 222934
 
2.5%
d 216983
 
2.4%
Other values (37) 2572321
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7513490
84.1%
Uppercase Letter 1260077
 
14.1%
Space Separator 159425
 
1.8%
Other Punctuation 2788
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1191958
15.9%
i 1019910
13.6%
o 860340
11.5%
n 666504
8.9%
r 649356
8.6%
e 535220
7.1%
s 502495
6.7%
l 497759
6.6%
d 216983
 
2.9%
t 211080
 
2.8%
Other values (14) 1161885
15.5%
Uppercase Letter
ValueCountFrequency (%)
C 222934
17.7%
N 170114
13.5%
T 139500
11.1%
M 113544
9.0%
F 104736
8.3%
I 80821
 
6.4%
Y 58634
 
4.7%
G 57235
 
4.5%
A 47798
 
3.8%
W 43054
 
3.4%
Other values (11) 221707
17.6%
Space Separator
ValueCountFrequency (%)
159425
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8773567
98.2%
Common 162213
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1191958
13.6%
i 1019910
11.6%
o 860340
 
9.8%
n 666504
 
7.6%
r 649356
 
7.4%
e 535220
 
6.1%
s 502495
 
5.7%
l 497759
 
5.7%
C 222934
 
2.5%
d 216983
 
2.5%
Other values (35) 2410108
27.5%
Common
ValueCountFrequency (%)
159425
98.3%
. 2788
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8935780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1191958
13.3%
i 1019910
 
11.4%
o 860340
 
9.6%
n 666504
 
7.5%
r 649356
 
7.3%
e 535220
 
6.0%
s 502495
 
5.6%
l 497759
 
5.6%
C 222934
 
2.5%
d 216983
 
2.4%
Other values (37) 2572321
28.8%

destwac
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.46602283
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:44.363793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median44
Q381
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)47

Descriptive statistics

Standard deviation26.54359849
Coefficient of variation (CV)0.4873423304
Kurtosis-1.306755406
Mean54.46602283
Median Absolute Deviation (MAD)22
Skewness-0.008660971942
Sum59882342
Variance704.562621
MonotonicityNot monotonic
2023-12-06T17:31:44.563969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 118133
 
10.7%
91 113390
 
10.3%
33 104736
 
9.5%
22 58634
 
5.3%
41 58323
 
5.3%
34 57235
 
5.2%
82 49361
 
4.5%
36 47366
 
4.3%
38 39892
 
3.6%
81 33629
 
3.1%
Other values (43) 418745
38.1%
ValueCountFrequency (%)
1 5887
 
0.5%
2 20361
1.9%
3 5318
 
0.5%
4 1208
 
0.1%
5 186
 
< 0.1%
ValueCountFrequency (%)
93 31889
 
2.9%
92 13323
 
1.2%
91 113390
10.3%
88 1711
 
0.2%
87 19685
 
1.8%

crsdeptime
Real number (ℝ)

Distinct1279
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1327.458824
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:44.764194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile605
Q1915
median1317
Q31730
95-th percentile2120
Maximum2359
Range2358
Interquartile range (IQR)815

Descriptive statistics

Standard deviation486.8915854
Coefficient of variation (CV)0.3667846993
Kurtosis-1.01264091
Mean1327.458824
Median Absolute Deviation (MAD)408
Skewness0.08763143931
Sum1459466639
Variance237063.416
MonotonicityNot monotonic
2023-12-06T17:31:44.976695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 26731
 
2.4%
700 19452
 
1.8%
800 12795
 
1.2%
900 8572
 
0.8%
1000 8206
 
0.7%
1100 7510
 
0.7%
830 7078
 
0.6%
730 6990
 
0.6%
630 6667
 
0.6%
715 6638
 
0.6%
Other values (1269) 988805
89.9%
ValueCountFrequency (%)
1 13
 
< 0.1%
2 1
 
< 0.1%
3 9
 
< 0.1%
4 12
 
< 0.1%
5 39
< 0.1%
ValueCountFrequency (%)
2359 1541
0.1%
2358 118
 
< 0.1%
2357 10
 
< 0.1%
2356 47
 
< 0.1%
2355 981
0.1%

deptime
Real number (ℝ)

MISSING 

Distinct1439
Distinct (%)0.1%
Missing16322
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1332.240785
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:45.169513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile602
Q1919
median1325
Q31739
95-th percentile2131
Maximum2400
Range2399
Interquartile range (IQR)820

Descriptive statistics

Standard deviation498.3627784
Coefficient of variation (CV)0.3740786081
Kurtosis-0.9531190789
Mean1332.240785
Median Absolute Deviation (MAD)410
Skewness0.03503251633
Sum1442979303
Variance248365.4589
MonotonicityNot monotonic
2023-12-06T17:31:45.380292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
557 3026
 
0.3%
555 3000
 
0.3%
559 2886
 
0.3%
556 2866
 
0.3%
558 2844
 
0.3%
600 2654
 
0.2%
655 2356
 
0.2%
554 2236
 
0.2%
656 2235
 
0.2%
657 2159
 
0.2%
Other values (1429) 1056860
96.1%
(Missing) 16322
 
1.5%
ValueCountFrequency (%)
1 164
< 0.1%
2 125
< 0.1%
3 121
< 0.1%
4 133
< 0.1%
5 99
< 0.1%
ValueCountFrequency (%)
2400 106
< 0.1%
2359 202
< 0.1%
2358 212
< 0.1%
2357 190
< 0.1%
2356 196
< 0.1%

depdelay
Real number (ℝ)

MISSING  ZEROS 

Distinct1217
Distinct (%)0.1%
Missing16335
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean10.50007986
Minimum-60
Maximum3001
Zeros59738
Zeros (%)5.4%
Negative609489
Negative (%)55.4%
Memory size8.4 MiB
2023-12-06T17:31:45.580465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-10
Q1-5
median-1
Q39
95-th percentile67
Maximum3001
Range3061
Interquartile range (IQR)14

Descriptive statistics

Standard deviation46.8931308
Coefficient of variation (CV)4.465978489
Kurtosis264.3177919
Mean10.50007986
Median Absolute Deviation (MAD)5
Skewness11.94645777
Sum11372731
Variance2198.965716
MonotonicityNot monotonic
2023-12-06T17:31:45.780158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 83077
 
7.6%
-4 79793
 
7.3%
-3 79287
 
7.2%
-2 73092
 
6.6%
-1 67968
 
6.2%
-6 60554
 
5.5%
0 59738
 
5.4%
-7 47753
 
4.3%
-8 36799
 
3.3%
1 26740
 
2.4%
Other values (1207) 468308
42.6%
ValueCountFrequency (%)
-60 1
< 0.1%
-57 1
< 0.1%
-49 1
< 0.1%
-47 1
< 0.1%
-45 1
< 0.1%
ValueCountFrequency (%)
3001 1
< 0.1%
2574 1
< 0.1%
2439 1
< 0.1%
2369 1
< 0.1%
2232 1
< 0.1%

depdelayminutes
Real number (ℝ)

MISSING  ZEROS 

Distinct1170
Distinct (%)0.1%
Missing16335
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean13.31613808
Minimum0
Maximum3001
Zeros669227
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:45.980627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile67
Maximum3001
Range3001
Interquartile range (IQR)9

Descriptive statistics

Standard deviation45.96096479
Coefficient of variation (CV)3.451523596
Kurtosis283.5738831
Mean13.31613808
Median Absolute Deviation (MAD)0
Skewness12.52181441
Sum14422829
Variance2112.410285
MonotonicityNot monotonic
2023-12-06T17:31:46.184572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 669227
60.9%
1 26740
 
2.4%
2 21714
 
2.0%
3 19376
 
1.8%
4 17492
 
1.6%
5 16373
 
1.5%
6 14780
 
1.3%
7 13752
 
1.3%
8 12710
 
1.2%
9 11499
 
1.0%
Other values (1160) 259446
 
23.6%
(Missing) 16335
 
1.5%
ValueCountFrequency (%)
0 669227
60.9%
1 26740
 
2.4%
2 21714
 
2.0%
3 19376
 
1.8%
4 17492
 
1.6%
ValueCountFrequency (%)
3001 1
< 0.1%
2574 1
< 0.1%
2439 1
< 0.1%
2369 1
< 0.1%
2232 1
< 0.1%

depdel15
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing16335
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.1950006878
Minimum0
Maximum1
Zeros871902
Zeros (%)79.3%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:46.346643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3962014191
Coefficient of variation (CV)2.031794982
Kurtosis0.3704313752
Mean0.1950006878
Median Absolute Deviation (MAD)0
Skewness1.539620307
Sum211207
Variance0.1569755645
MonotonicityNot monotonic
2023-12-06T17:31:46.514188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 871902
79.3%
1 211207
 
19.2%
(Missing) 16335
 
1.5%
ValueCountFrequency (%)
0 871902
79.3%
1 211207
 
19.2%
ValueCountFrequency (%)
1 211207
 
19.2%
0 871902
79.3%

departuredelaygroups
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing16335
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.06546709519
Minimum-2
Maximum12
Zeros262413
Zeros (%)23.9%
Negative609489
Negative (%)55.4%
Memory size8.4 MiB
2023-12-06T17:31:46.662813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q1-1
median-1
Q30
95-th percentile4
Maximum12
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.097080348
Coefficient of variation (CV)32.03258587
Kurtosis13.5607676
Mean0.06546709519
Median Absolute Deviation (MAD)0
Skewness3.40371369
Sum70908
Variance4.397745986
MonotonicityNot monotonic
2023-12-06T17:31:46.813242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-1 605992
55.1%
0 262413
23.9%
1 82050
 
7.5%
2 41731
 
3.8%
3 24404
 
2.2%
4 15843
 
1.4%
12 10708
 
1.0%
5 10643
 
1.0%
6 7759
 
0.7%
7 5793
 
0.5%
Other values (5) 15773
 
1.4%
(Missing) 16335
 
1.5%
ValueCountFrequency (%)
-2 3497
 
0.3%
-1 605992
55.1%
0 262413
23.9%
1 82050
 
7.5%
2 41731
 
3.8%
ValueCountFrequency (%)
12 10708
1.0%
11 2085
 
0.2%
10 2629
 
0.2%
9 3261
 
0.3%
8 4301
0.4%
Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:47.065234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9894996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1800-1859
2nd row1800-1859
3rd row1800-1859
4th row1800-1859
5th row1800-1859
ValueCountFrequency (%)
0600-0659 77411
 
7.0%
0800-0859 75937
 
6.9%
0700-0759 75588
 
6.9%
1000-1059 74850
 
6.8%
1200-1259 70901
 
6.4%
1700-1759 69538
 
6.3%
1500-1559 67025
 
6.1%
1300-1359 66830
 
6.1%
1800-1859 66531
 
6.1%
1100-1159 66464
 
6.0%
Other values (9) 388369
35.3%
2023-12-06T17:31:47.462312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3077790
31.1%
1 1558123
15.7%
9 1336220
13.5%
5 1259459
12.7%
- 1099444
 
11.1%
2 430282
 
4.3%
7 290252
 
2.9%
8 284936
 
2.9%
6 279516
 
2.8%
3 152264
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8795552
88.9%
Dash Punctuation 1099444
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3077790
35.0%
1 1558123
17.7%
9 1336220
15.2%
5 1259459
14.3%
2 430282
 
4.9%
7 290252
 
3.3%
8 284936
 
3.2%
6 279516
 
3.2%
3 152264
 
1.7%
4 126710
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 1099444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9894996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3077790
31.1%
1 1558123
15.7%
9 1336220
13.5%
5 1259459
12.7%
- 1099444
 
11.1%
2 430282
 
4.3%
7 290252
 
2.9%
8 284936
 
2.9%
6 279516
 
2.8%
3 152264
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9894996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3077790
31.1%
1 1558123
15.7%
9 1336220
13.5%
5 1259459
12.7%
- 1099444
 
11.1%
2 430282
 
4.3%
7 290252
 
2.9%
8 284936
 
2.9%
6 279516
 
2.8%
3 152264
 
1.5%

taxiout
Real number (ℝ)

MISSING 

Distinct162
Distinct (%)< 0.1%
Missing16558
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean16.4903277
Minimum1
Maximum218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:47.813802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median14
Q319
95-th percentile32
Maximum218
Range217
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.253558293
Coefficient of variation (CV)0.5005090526
Kurtosis18.20641296
Mean16.4903277
Median Absolute Deviation (MAD)3
Skewness2.917544348
Sum17857145
Variance68.1212245
MonotonicityNot monotonic
2023-12-06T17:31:47.996366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 91753
 
8.3%
13 89510
 
8.1%
11 87906
 
8.0%
14 82811
 
7.5%
10 75978
 
6.9%
15 73396
 
6.7%
16 64104
 
5.8%
9 57086
 
5.2%
17 55194
 
5.0%
18 47190
 
4.3%
Other values (152) 357958
32.6%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 29
 
< 0.1%
3 168
 
< 0.1%
4 573
 
0.1%
5 1913
0.2%
ValueCountFrequency (%)
218 1
< 0.1%
215 1
< 0.1%
200 1
< 0.1%
192 1
< 0.1%
182 1
< 0.1%

wheelsoff
Real number (ℝ)

MISSING 

Distinct1438
Distinct (%)0.1%
Missing16558
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1353.995904
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:48.204744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile618
Q1934
median1337
Q31752
95-th percentile2142
Maximum2400
Range2399
Interquartile range (IQR)818

Descriptive statistics

Standard deviation499.5686536
Coefficient of variation (CV)0.3689587629
Kurtosis-0.8807710084
Mean1353.995904
Median Absolute Deviation (MAD)409
Skewness-0.001146316098
Sum1466223208
Variance249568.8397
MonotonicityNot monotonic
2023-12-06T17:31:48.413451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 1920
 
0.2%
610 1906
 
0.2%
609 1896
 
0.2%
608 1844
 
0.2%
612 1832
 
0.2%
613 1773
 
0.2%
614 1762
 
0.2%
607 1680
 
0.2%
615 1627
 
0.1%
710 1567
 
0.1%
Other values (1428) 1065079
96.9%
(Missing) 16558
 
1.5%
ValueCountFrequency (%)
1 240
< 0.1%
2 192
< 0.1%
3 199
< 0.1%
4 198
< 0.1%
5 173
< 0.1%
ValueCountFrequency (%)
2400 143
< 0.1%
2359 176
< 0.1%
2358 176
< 0.1%
2357 165
< 0.1%
2356 146
< 0.1%

wheelson
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.1%
Missing17189
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1470.206553
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:48.611772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile659
Q11057
median1505
Q31912
95-th percentile2249
Maximum2400
Range2399
Interquartile range (IQR)855

Descriptive statistics

Standard deviation524.8289383
Coefficient of variation (CV)0.3569763291
Kurtosis-0.4126611742
Mean1470.206553
Median Absolute Deviation (MAD)410
Skewness-0.3182871515
Sum1591138393
Variance275445.4144
MonotonicityNot monotonic
2023-12-06T17:31:48.813645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1621 1223
 
0.1%
1631 1216
 
0.1%
1652 1210
 
0.1%
1625 1209
 
0.1%
1138 1208
 
0.1%
1140 1207
 
0.1%
1121 1205
 
0.1%
1142 1202
 
0.1%
1637 1201
 
0.1%
1341 1200
 
0.1%
Other values (1430) 1070174
97.3%
(Missing) 17189
 
1.6%
ValueCountFrequency (%)
1 602
0.1%
2 490
< 0.1%
3 499
< 0.1%
4 455
< 0.1%
5 501
< 0.1%
ValueCountFrequency (%)
2400 498
< 0.1%
2359 504
< 0.1%
2358 589
0.1%
2357 581
0.1%
2356 564
0.1%

taxiin
Real number (ℝ)

MISSING 

Distinct167
Distinct (%)< 0.1%
Missing17189
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean7.938672494
Minimum1
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:48.996397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile19
Maximum191
Range190
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.662458687
Coefficient of variation (CV)0.8392409048
Kurtosis42.96679015
Mean7.938672494
Median Absolute Deviation (MAD)2
Skewness4.64183663
Sum8591668
Variance44.38835576
MonotonicityNot monotonic
2023-12-06T17:31:49.225448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 163734
14.9%
5 155519
14.1%
6 127349
11.6%
3 109378
9.9%
7 102073
9.3%
8 77219
7.0%
9 60018
 
5.5%
10 47292
 
4.3%
11 36601
 
3.3%
2 30676
 
2.8%
Other values (157) 172396
15.7%
ValueCountFrequency (%)
1 1797
 
0.2%
2 30676
 
2.8%
3 109378
9.9%
4 163734
14.9%
5 155519
14.1%
ValueCountFrequency (%)
191 1
< 0.1%
189 1
< 0.1%
186 1
< 0.1%
185 1
< 0.1%
181 1
< 0.1%

crsarrtime
Real number (ℝ)

Distinct1386
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494.724781
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:49.413333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile726
Q11113
median1518
Q31923
95-th percentile2257
Maximum2400
Range2399
Interquartile range (IQR)810

Descriptive statistics

Standard deviation512.0199619
Coefficient of variation (CV)0.3425513302
Kurtosis-0.4476243222
Mean1494.724781
Median Absolute Deviation (MAD)405
Skewness-0.2774897539
Sum1643366192
Variance262164.4414
MonotonicityNot monotonic
2023-12-06T17:31:49.629414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3816
 
0.3%
950 3281
 
0.3%
2200 3206
 
0.3%
1400 3153
 
0.3%
2100 3039
 
0.3%
1640 3029
 
0.3%
1200 2954
 
0.3%
1810 2948
 
0.3%
945 2834
 
0.3%
1000 2831
 
0.3%
Other values (1376) 1068353
97.2%
ValueCountFrequency (%)
1 264
 
< 0.1%
2 310
< 0.1%
3 313
< 0.1%
4 294
 
< 0.1%
5 748
0.1%
ValueCountFrequency (%)
2400 7
 
< 0.1%
2359 3816
0.3%
2358 1458
 
0.1%
2357 1099
 
0.1%
2356 965
 
0.1%

arrtime
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.1%
Missing17189
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1473.75772
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:49.845480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile658
Q11100
median1509
Q31917
95-th percentile2251
Maximum2400
Range2399
Interquartile range (IQR)817

Descriptive statistics

Standard deviation530.1188707
Coefficient of variation (CV)0.3597055768
Kurtosis-0.3256458693
Mean1473.75772
Median Absolute Deviation (MAD)409
Skewness-0.3634068668
Sum1594981661
Variance281026.0171
MonotonicityNot monotonic
2023-12-06T17:31:50.028999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1145 1251
 
0.1%
1351 1249
 
0.1%
1139 1237
 
0.1%
1141 1233
 
0.1%
1638 1231
 
0.1%
1628 1221
 
0.1%
1634 1220
 
0.1%
1150 1217
 
0.1%
1655 1211
 
0.1%
1149 1209
 
0.1%
Other values (1430) 1069976
97.3%
(Missing) 17189
 
1.6%
ValueCountFrequency (%)
1 687
0.1%
2 598
0.1%
3 577
0.1%
4 560
0.1%
5 594
0.1%
ValueCountFrequency (%)
2400 581
0.1%
2359 645
0.1%
2358 669
0.1%
2357 684
0.1%
2356 689
0.1%

arrdelay
Real number (ℝ)

MISSING  ZEROS 

Distinct1233
Distinct (%)0.1%
Missing18860
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean3.749767718
Minimum-82
Maximum2976
Zeros20607
Zeros (%)1.9%
Negative680926
Negative (%)61.9%
Memory size8.4 MiB
2023-12-06T17:31:50.231419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-82
5-th percentile-27
Q1-15
median-6
Q37
95-th percentile64
Maximum2976
Range3058
Interquartile range (IQR)22

Descriptive statistics

Standard deviation48.44809595
Coefficient of variation (CV)12.92029256
Kurtosis230.2070482
Mean3.749767718
Median Absolute Deviation (MAD)11
Skewness10.77866373
Sum4051939
Variance2347.218001
MonotonicityNot monotonic
2023-12-06T17:31:50.430688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 30634
 
2.8%
-11 30579
 
2.8%
-12 30490
 
2.8%
-10 30464
 
2.8%
-13 29816
 
2.7%
-8 29504
 
2.7%
-14 29377
 
2.7%
-7 28832
 
2.6%
-15 28047
 
2.6%
-6 27820
 
2.5%
Other values (1223) 785021
71.4%
ValueCountFrequency (%)
-82 1
 
< 0.1%
-76 1
 
< 0.1%
-75 1
 
< 0.1%
-74 2
< 0.1%
-73 4
< 0.1%
ValueCountFrequency (%)
2976 1
< 0.1%
2565 1
< 0.1%
2416 1
< 0.1%
2371 1
< 0.1%
2221 1
< 0.1%

arrdelayminutes
Real number (ℝ)

MISSING  ZEROS 

Distinct1156
Distinct (%)0.1%
Missing18860
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean12.59359013
Minimum0
Maximum2976
Zeros701533
Zeros (%)63.8%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:50.631173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile64
Maximum2976
Range2976
Interquartile range (IQR)7

Descriptive statistics

Standard deviation45.04303188
Coefficient of variation (CV)3.576663319
Kurtosis298.7289445
Mean12.59359013
Median Absolute Deviation (MAD)0
Skewness12.88759869
Sum13608432
Variance2028.874721
MonotonicityNot monotonic
2023-12-06T17:31:50.879839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 701533
63.8%
1 18820
 
1.7%
2 17990
 
1.6%
3 16647
 
1.5%
4 15549
 
1.4%
5 14443
 
1.3%
6 13626
 
1.2%
7 12615
 
1.1%
8 11702
 
1.1%
9 11037
 
1.0%
Other values (1146) 246622
 
22.4%
(Missing) 18860
 
1.7%
ValueCountFrequency (%)
0 701533
63.8%
1 18820
 
1.7%
2 17990
 
1.6%
3 16647
 
1.5%
4 15549
 
1.4%
ValueCountFrequency (%)
2976 1
< 0.1%
2565 1
< 0.1%
2416 1
< 0.1%
2371 1
< 0.1%
2221 1
< 0.1%

arrdel15
Real number (ℝ)

MISSING  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing18860
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.1858467273
Minimum0
Maximum1
Zeros879761
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:51.078813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3889831118
Coefficient of variation (CV)2.093031809
Kurtosis0.6090564194
Mean0.1858467273
Median Absolute Deviation (MAD)0
Skewness1.615257036
Sum200823
Variance0.1513078613
MonotonicityNot monotonic
2023-12-06T17:31:51.246301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 879761
80.0%
1 200823
 
18.3%
(Missing) 18860
 
1.7%
ValueCountFrequency (%)
0 879761
80.0%
1 200823
 
18.3%
ValueCountFrequency (%)
1 200823
 
18.3%
0 879761
80.0%

arrivaldelaygroups
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing18860
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean-0.2771538353
Minimum-2
Maximum12
Zeros198835
Zeros (%)18.1%
Negative680926
Negative (%)61.9%
Memory size8.4 MiB
2023-12-06T17:31:51.412940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median-1
Q30
95-th percentile4
Maximum12
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.213480029
Coefficient of variation (CV)-7.986467251
Kurtosis11.53618361
Mean-0.2771538353
Median Absolute Deviation (MAD)1
Skewness3.025332062
Sum-299488
Variance4.89949384
MonotonicityNot monotonic
2023-12-06T17:31:51.578294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-1 417612
38.0%
-2 263314
23.9%
0 198835
18.1%
1 79811
 
7.3%
2 39090
 
3.6%
3 22868
 
2.1%
4 14750
 
1.3%
12 10141
 
0.9%
5 10055
 
0.9%
6 7219
 
0.7%
Other values (5) 16889
 
1.5%
(Missing) 18860
 
1.7%
ValueCountFrequency (%)
-2 263314
23.9%
-1 417612
38.0%
0 198835
18.1%
1 79811
 
7.3%
2 39090
 
3.6%
ValueCountFrequency (%)
12 10141
0.9%
11 1928
 
0.2%
10 2507
 
0.2%
9 3183
 
0.3%
8 4054
 
0.4%
Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:51.859269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9894996
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1900-1959
2nd row1900-1959
3rd row1900-1959
4th row1900-1959
5th row1900-1959
ValueCountFrequency (%)
1100-1159 70044
 
6.4%
1400-1459 69502
 
6.3%
1600-1659 69424
 
6.3%
1200-1259 68843
 
6.3%
1700-1759 67583
 
6.1%
0900-0959 67320
 
6.1%
1800-1859 66414
 
6.0%
1500-1559 65733
 
6.0%
2100-2159 64896
 
5.9%
1300-1359 63671
 
5.8%
Other values (9) 426014
38.7%
2023-12-06T17:31:52.311318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2845110
28.8%
1 1630402
16.5%
9 1360436
13.7%
5 1256566
12.7%
- 1099444
 
11.1%
2 716526
 
7.2%
3 232474
 
2.3%
8 231388
 
2.3%
7 214612
 
2.2%
6 169034
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8795552
88.9%
Dash Punctuation 1099444
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2845110
32.3%
1 1630402
18.5%
9 1360436
15.5%
5 1256566
14.3%
2 716526
 
8.1%
3 232474
 
2.6%
8 231388
 
2.6%
7 214612
 
2.4%
6 169034
 
1.9%
4 139004
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 1099444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9894996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2845110
28.8%
1 1630402
16.5%
9 1360436
13.7%
5 1256566
12.7%
- 1099444
 
11.1%
2 716526
 
7.2%
3 232474
 
2.3%
8 231388
 
2.3%
7 214612
 
2.2%
6 169034
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9894996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2845110
28.8%
1 1630402
16.5%
9 1360436
13.7%
5 1256566
12.7%
- 1099444
 
11.1%
2 716526
 
7.2%
3 232474
 
2.3%
8 231388
 
2.3%
7 214612
 
2.2%
6 169034
 
1.7%

cancelled
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01523770197
Minimum0
Maximum1
Zeros1082691
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:52.494250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1224970532
Coefficient of variation (CV)8.039076595
Kurtosis60.64244845
Mean0.01523770197
Median Absolute Deviation (MAD)0
Skewness7.914691285
Sum16753
Variance0.01500552805
MonotonicityNot monotonic
2023-12-06T17:31:52.681720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1082691
98.5%
1 16753
 
1.5%
ValueCountFrequency (%)
0 1082691
98.5%
1 16753
 
1.5%
ValueCountFrequency (%)
1 16753
 
1.5%
0 1082691
98.5%

cancellationcode
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1082691
Missing (%)98.5%
Memory size8.4 MiB
2023-12-06T17:31:52.794591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16753
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowB
ValueCountFrequency (%)
a 10880
64.9%
b 5458
32.6%
c 408
 
2.4%
d 7
 
< 0.1%
2023-12-06T17:31:53.061326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10880
64.9%
B 5458
32.6%
C 408
 
2.4%
D 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16753
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 10880
64.9%
B 5458
32.6%
C 408
 
2.4%
D 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 16753
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 10880
64.9%
B 5458
32.6%
C 408
 
2.4%
D 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 10880
64.9%
B 5458
32.6%
C 408
 
2.4%
D 7
 
< 0.1%

diverted
Real number (ℝ)

SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00191642321
Minimum0
Maximum1
Zeros1097337
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:53.214612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0437350234
Coefficient of variation (CV)22.82117184
Kurtosis516.8096864
Mean0.00191642321
Median Absolute Deviation (MAD)0
Skewness22.77737356
Sum2107
Variance0.001912752272
MonotonicityNot monotonic
2023-12-06T17:31:53.344738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1097337
99.8%
1 2107
 
0.2%
ValueCountFrequency (%)
0 1097337
99.8%
1 2107
 
0.2%
ValueCountFrequency (%)
1 2107
 
0.2%
0 1097337
99.8%

crselapsedtime
Real number (ℝ)

Distinct488
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.4785301
Minimum-56
Maximum685
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size8.4 MiB
2023-12-06T17:31:53.510860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-56
5-th percentile65
Q192
median130
Q3175
95-th percentile304
Maximum685
Range741
Interquartile range (IQR)83

Descriptive statistics

Standard deviation72.8280322
Coefficient of variation (CV)0.5006101737
Kurtosis2.741909972
Mean145.4785301
Median Absolute Deviation (MAD)41
Skewness1.445407966
Sum159945497
Variance5303.922274
MonotonicityNot monotonic
2023-12-06T17:31:53.714726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 19758
 
1.8%
90 18378
 
1.7%
80 17320
 
1.6%
75 16219
 
1.5%
70 14359
 
1.3%
95 14351
 
1.3%
110 13884
 
1.3%
135 12979
 
1.2%
120 12590
 
1.1%
140 12316
 
1.1%
Other values (478) 947290
86.2%
ValueCountFrequency (%)
-56 1
 
< 0.1%
22 26
< 0.1%
24 29
< 0.1%
25 18
< 0.1%
26 30
< 0.1%
ValueCountFrequency (%)
685 25
< 0.1%
680 17
< 0.1%
678 1
 
< 0.1%
676 16
< 0.1%
675 31
< 0.1%

actualelapsedtime
Real number (ℝ)

MISSING 

Distinct685
Distinct (%)0.1%
Missing18860
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean138.6932816
Minimum15
Maximum736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:53.898617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile57
Q186
median123
Q3170
95-th percentile294
Maximum736
Range721
Interquartile range (IQR)84

Descriptive statistics

Standard deviation72.53919958
Coefficient of variation (CV)0.5230188423
Kurtosis2.806863362
Mean138.6932816
Median Absolute Deviation (MAD)41
Skewness1.444872758
Sum149869741
Variance5261.935475
MonotonicityNot monotonic
2023-12-06T17:31:54.078186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 8500
 
0.8%
82 8487
 
0.8%
77 8450
 
0.8%
83 8407
 
0.8%
84 8382
 
0.8%
80 8305
 
0.8%
79 8276
 
0.8%
85 8207
 
0.7%
76 8203
 
0.7%
75 8198
 
0.7%
Other values (675) 997169
90.7%
(Missing) 18860
 
1.7%
ValueCountFrequency (%)
15 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 9
< 0.1%
19 6
< 0.1%
ValueCountFrequency (%)
736 1
< 0.1%
733 1
< 0.1%
720 1
< 0.1%
719 1
< 0.1%
712 1
< 0.1%

airtime
Real number (ℝ)

MISSING 

Distinct659
Distinct (%)0.1%
Missing18860
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean114.2721704
Minimum8
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:54.294435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile35
Q162
median98
Q3144
95-th percentile267
Maximum711
Range703
Interquartile range (IQR)82

Descriptive statistics

Standard deviation70.84978529
Coefficient of variation (CV)0.6200090979
Kurtosis2.896997567
Mean114.2721704
Median Absolute Deviation (MAD)40
Skewness1.47915917
Sum123480679
Variance5019.692076
MonotonicityNot monotonic
2023-12-06T17:31:54.627011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 9035
 
0.8%
64 9027
 
0.8%
44 8982
 
0.8%
65 8976
 
0.8%
63 8921
 
0.8%
62 8904
 
0.8%
60 8893
 
0.8%
55 8876
 
0.8%
59 8819
 
0.8%
57 8735
 
0.8%
Other values (649) 991416
90.2%
(Missing) 18860
 
1.7%
ValueCountFrequency (%)
8 4
 
< 0.1%
9 32
< 0.1%
10 38
< 0.1%
11 18
< 0.1%
14 17
< 0.1%
ValueCountFrequency (%)
711 1
< 0.1%
687 2
< 0.1%
686 1
< 0.1%
678 1
< 0.1%
673 1
< 0.1%

flights
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:54.809785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum1099444
Variance0
MonotonicityIncreasing
2023-12-06T17:31:54.927523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 1099444
100.0%
ValueCountFrequency (%)
1 1099444
100.0%
ValueCountFrequency (%)
1 1099444
100.0%

distance
Real number (ℝ)

Distinct1516
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824.6117829
Minimum31
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:55.094614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1391
median679
Q31065
95-th percentile2174
Maximum5095
Range5064
Interquartile range (IQR)674

Descriptive statistics

Standard deviation595.8278228
Coefficient of variation (CV)0.722555553
Kurtosis3.09657068
Mean824.6117829
Median Absolute Deviation (MAD)330
Skewness1.519275858
Sum906614477
Variance355010.7944
MonotonicityNot monotonic
2023-12-06T17:31:55.276821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399 6230
 
0.6%
337 5691
 
0.5%
296 5354
 
0.5%
224 4770
 
0.4%
733 4614
 
0.4%
862 4577
 
0.4%
594 4524
 
0.4%
391 4523
 
0.4%
404 4377
 
0.4%
867 3992
 
0.4%
Other values (1506) 1050792
95.6%
ValueCountFrequency (%)
31 118
 
< 0.1%
66 122
 
< 0.1%
67 467
< 0.1%
68 135
 
< 0.1%
71 2
 
< 0.1%
ValueCountFrequency (%)
5095 90
< 0.1%
4983 120
< 0.1%
4962 122
< 0.1%
4904 44
 
< 0.1%
4817 44
 
< 0.1%

distancegroup
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.769039624
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:55.427606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.330073893
Coefficient of variation (CV)0.6182142206
Kurtosis1.303923894
Mean3.769039624
Median Absolute Deviation (MAD)1
Skewness1.236940186
Sum4143848
Variance5.429244348
MonotonicityNot monotonic
2023-12-06T17:31:55.582703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 249356
22.7%
3 215720
19.6%
4 182120
16.6%
1 134640
12.2%
5 132343
12.0%
6 49956
 
4.5%
7 47809
 
4.3%
10 26941
 
2.5%
11 22980
 
2.1%
8 21659
 
2.0%
ValueCountFrequency (%)
1 134640
12.2%
2 249356
22.7%
3 215720
19.6%
4 182120
16.6%
5 132343
12.0%
ValueCountFrequency (%)
11 22980
2.1%
10 26941
2.5%
9 15920
 
1.4%
8 21659
2.0%
7 47809
4.3%

carrierdelay
Real number (ℝ)

MISSING  ZEROS 

Distinct1028
Distinct (%)0.5%
Missing898621
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean27.89930934
Minimum0
Maximum2976
Zeros70542
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:55.744776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q327
95-th percentile110
Maximum2976
Range2976
Interquartile range (IQR)27

Descriptive statistics

Standard deviation73.37838885
Coefficient of variation (CV)2.630114887
Kurtosis153.8911316
Mean27.89930934
Median Absolute Deviation (MAD)9
Skewness9.74809191
Sum5602823
Variance5384.38795
MonotonicityNot monotonic
2023-12-06T17:31:55.944722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70542
 
6.4%
15 4312
 
0.4%
16 3975
 
0.4%
6 3680
 
0.3%
17 3654
 
0.3%
7 3505
 
0.3%
1 3423
 
0.3%
18 3370
 
0.3%
3 3368
 
0.3%
4 3302
 
0.3%
Other values (1018) 97692
 
8.9%
(Missing) 898621
81.7%
ValueCountFrequency (%)
0 70542
6.4%
1 3423
 
0.3%
2 3243
 
0.3%
3 3368
 
0.3%
4 3302
 
0.3%
ValueCountFrequency (%)
2976 1
< 0.1%
2565 1
< 0.1%
2416 1
< 0.1%
2365 1
< 0.1%
2221 1
< 0.1%

weatherdelay
Real number (ℝ)

MISSING  ZEROS 

Distinct540
Distinct (%)0.3%
Missing898621
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean3.100695637
Minimum0
Maximum1392
Zeros193041
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:56.126966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1392
Range1392
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.4744164
Coefficient of variation (CV)9.505743178
Kurtosis539.5258848
Mean3.100695637
Median Absolute Deviation (MAD)0
Skewness19.77983184
Sum622691
Variance868.7412222
MonotonicityNot monotonic
2023-12-06T17:31:56.327304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193041
 
17.6%
16 192
 
< 0.1%
15 157
 
< 0.1%
19 153
 
< 0.1%
17 150
 
< 0.1%
18 137
 
< 0.1%
6 129
 
< 0.1%
23 126
 
< 0.1%
24 122
 
< 0.1%
9 121
 
< 0.1%
Other values (530) 6495
 
0.6%
(Missing) 898621
81.7%
ValueCountFrequency (%)
0 193041
17.6%
1 82
 
< 0.1%
2 93
 
< 0.1%
3 89
 
< 0.1%
4 98
 
< 0.1%
ValueCountFrequency (%)
1392 1
< 0.1%
1371 1
< 0.1%
1306 1
< 0.1%
1245 1
< 0.1%
1222 1
< 0.1%

nasdelay
Real number (ℝ)

MISSING  ZEROS 

Distinct338
Distinct (%)0.2%
Missing898621
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean8.548806661
Minimum0
Maximum1147
Zeros116711
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:56.526798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile37
Maximum1147
Range1147
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.0829349
Coefficient of variation (CV)2.349209158
Kurtosis390.8574632
Mean8.548806661
Median Absolute Deviation (MAD)0
Skewness12.30881961
Sum1716797
Variance403.3242741
MonotonicityNot monotonic
2023-12-06T17:31:56.743743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116711
 
10.6%
1 4786
 
0.4%
15 3605
 
0.3%
2 3471
 
0.3%
16 3432
 
0.3%
3 3342
 
0.3%
4 3134
 
0.3%
17 3034
 
0.3%
5 2950
 
0.3%
6 2870
 
0.3%
Other values (328) 53488
 
4.9%
(Missing) 898621
81.7%
ValueCountFrequency (%)
0 116711
10.6%
1 4786
 
0.4%
2 3471
 
0.3%
3 3342
 
0.3%
4 3134
 
0.3%
ValueCountFrequency (%)
1147 1
< 0.1%
985 1
< 0.1%
969 1
< 0.1%
963 1
< 0.1%
935 1
< 0.1%

securitydelay
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct132
Distinct (%)0.1%
Missing898621
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean0.2136010318
Minimum0
Maximum983
Zeros199313
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:56.943567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum983
Range983
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.149683503
Coefficient of variation (CV)19.42726338
Kurtosis16477.81643
Mean0.2136010318
Median Absolute Deviation (MAD)0
Skewness85.93091604
Sum42896
Variance17.21987317
MonotonicityNot monotonic
2023-12-06T17:31:57.156732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199313
 
18.1%
15 71
 
< 0.1%
16 53
 
< 0.1%
19 46
 
< 0.1%
9 45
 
< 0.1%
17 45
 
< 0.1%
18 45
 
< 0.1%
12 44
 
< 0.1%
7 42
 
< 0.1%
23 41
 
< 0.1%
Other values (122) 1078
 
0.1%
(Missing) 898621
81.7%
ValueCountFrequency (%)
0 199313
18.1%
1 27
 
< 0.1%
2 31
 
< 0.1%
3 29
 
< 0.1%
4 28
 
< 0.1%
ValueCountFrequency (%)
983 1
< 0.1%
291 1
< 0.1%
277 1
< 0.1%
264 1
< 0.1%
238 1
< 0.1%

lateaircraftdelay
Real number (ℝ)

MISSING  ZEROS 

Distinct684
Distinct (%)0.3%
Missing898621
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean22.29295449
Minimum0
Maximum1615
Zeros106887
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:57.343363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile102
Maximum1615
Range1615
Interquartile range (IQR)25

Descriptive statistics

Standard deviation51.06507492
Coefficient of variation (CV)2.290637382
Kurtosis110.4185809
Mean22.29295449
Median Absolute Deviation (MAD)0
Skewness7.517970666
Sum4476938
Variance2607.641876
MonotonicityNot monotonic
2023-12-06T17:31:57.560154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106887
 
9.7%
15 2463
 
0.2%
16 2244
 
0.2%
17 2184
 
0.2%
19 2082
 
0.2%
18 2072
 
0.2%
20 1909
 
0.2%
14 1859
 
0.2%
21 1854
 
0.2%
13 1833
 
0.2%
Other values (674) 75436
 
6.9%
(Missing) 898621
81.7%
ValueCountFrequency (%)
0 106887
9.7%
1 1429
 
0.1%
2 1434
 
0.1%
3 1387
 
0.1%
4 1450
 
0.1%
ValueCountFrequency (%)
1615 1
< 0.1%
1523 1
< 0.1%
1520 1
< 0.1%
1446 1
< 0.1%
1439 1
< 0.1%

firstdeptime
Real number (ℝ)

MISSING 

Distinct1176
Distinct (%)22.4%
Missing1094184
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean1314.428897
Minimum2
Maximum2358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:57.760006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile559
Q1835
median1308
Q31748
95-th percentile2156
Maximum2358
Range2356
Interquartile range (IQR)913

Descriptive statistics

Standard deviation532.4216148
Coefficient of variation (CV)0.4050592739
Kurtosis-0.9740487852
Mean1314.428897
Median Absolute Deviation (MAD)459
Skewness0.0520196668
Sum6913896
Variance283472.7759
MonotonicityNot monotonic
2023-12-06T17:31:57.961454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 19
 
< 0.1%
556 17
 
< 0.1%
557 16
 
< 0.1%
559 16
 
< 0.1%
655 15
 
< 0.1%
555 15
 
< 0.1%
657 15
 
< 0.1%
654 15
 
< 0.1%
805 14
 
< 0.1%
558 13
 
< 0.1%
Other values (1166) 5105
 
0.5%
(Missing) 1094184
99.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 3
< 0.1%
4 1
 
< 0.1%
6 3
< 0.1%
7 2
< 0.1%
ValueCountFrequency (%)
2358 4
< 0.1%
2357 1
 
< 0.1%
2356 2
< 0.1%
2355 2
< 0.1%
2354 1
 
< 0.1%

totaladdgtime
Real number (ℝ)

MISSING 

Distinct174
Distinct (%)3.3%
Missing1094187
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean31.75556401
Minimum1
Maximum278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:58.143103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116
median26
Q339
95-th percentile78
Maximum278
Range277
Interquartile range (IQR)23

Descriptive statistics

Standard deviation26.34703887
Coefficient of variation (CV)0.8296825987
Kurtosis14.97025126
Mean31.75556401
Median Absolute Deviation (MAD)12
Skewness2.984127985
Sum166939
Variance694.1664573
MonotonicityNot monotonic
2023-12-06T17:31:58.343342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 157
 
< 0.1%
14 151
 
< 0.1%
23 140
 
< 0.1%
26 136
 
< 0.1%
15 134
 
< 0.1%
16 131
 
< 0.1%
12 131
 
< 0.1%
18 130
 
< 0.1%
24 130
 
< 0.1%
30 129
 
< 0.1%
Other values (164) 3888
 
0.4%
(Missing) 1094187
99.5%
ValueCountFrequency (%)
1 49
< 0.1%
2 26
< 0.1%
3 46
< 0.1%
4 38
< 0.1%
5 52
< 0.1%
ValueCountFrequency (%)
278 1
< 0.1%
269 2
< 0.1%
260 1
< 0.1%
255 1
< 0.1%
254 1
< 0.1%

longestaddgtime
Real number (ℝ)

MISSING 

Distinct168
Distinct (%)3.2%
Missing1094187
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean31.3414495
Minimum1
Maximum278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:58.527005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116
median26
Q339
95-th percentile77
Maximum278
Range277
Interquartile range (IQR)23

Descriptive statistics

Standard deviation25.75751172
Coefficient of variation (CV)0.8218353692
Kurtosis15.93374638
Mean31.3414495
Median Absolute Deviation (MAD)11
Skewness3.046241891
Sum164762
Variance663.4494098
MonotonicityNot monotonic
2023-12-06T17:31:58.728292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 159
 
< 0.1%
14 154
 
< 0.1%
23 139
 
< 0.1%
26 136
 
< 0.1%
30 134
 
< 0.1%
15 134
 
< 0.1%
18 131
 
< 0.1%
16 131
 
< 0.1%
12 131
 
< 0.1%
24 130
 
< 0.1%
Other values (158) 3878
 
0.4%
(Missing) 1094187
99.5%
ValueCountFrequency (%)
1 49
< 0.1%
2 26
< 0.1%
3 46
< 0.1%
4 38
< 0.1%
5 52
< 0.1%
ValueCountFrequency (%)
278 1
< 0.1%
269 2
< 0.1%
260 1
< 0.1%
255 1
< 0.1%
254 1
< 0.1%

divairportlandings
Real number (ℝ)

SKEWED  ZEROS 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003529966056
Minimum0
Maximum9
Zeros1097142
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:58.876836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1277582648
Coefficient of variation (CV)36.19249103
Kurtosis4366.166569
Mean0.003529966056
Median Absolute Deviation (MAD)0
Skewness62.89861948
Sum3881
Variance0.01632217423
MonotonicityNot monotonic
2023-12-06T17:31:59.010748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 1097142
99.8%
1 2088
 
0.2%
9 195
 
< 0.1%
2 19
 
< 0.1%
ValueCountFrequency (%)
0 1097142
99.8%
1 2088
 
0.2%
2 19
 
< 0.1%
9 195
 
< 0.1%
ValueCountFrequency (%)
9 195
 
< 0.1%
2 19
 
< 0.1%
1 2088
 
0.2%
0 1097142
99.8%

divreacheddest
Real number (ℝ)

MISSING 

Distinct2
Distinct (%)0.1%
Missing1097337
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean0.7930707167
Minimum0
Maximum1
Zeros436
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:31:59.143055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4052005427
Coefficient of variation (CV)0.5109261182
Kurtosis0.09656571329
Mean0.7930707167
Median Absolute Deviation (MAD)0
Skewness-1.447920907
Sum1671
Variance0.1641874798
MonotonicityNot monotonic
2023-12-06T17:31:59.309310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 1671
 
0.2%
0 436
 
< 0.1%
(Missing) 1097337
99.8%
ValueCountFrequency (%)
0 436
 
< 0.1%
1 1671
0.2%
ValueCountFrequency (%)
1 1671
0.2%
0 436
 
< 0.1%

divactualelapsedtime
Real number (ℝ)

MISSING 

Distinct618
Distinct (%)37.0%
Missing1097773
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean423.5332136
Minimum-95
Maximum1948
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size8.4 MiB
2023-12-06T17:31:59.476192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-95
5-th percentile189
Q1272.5
median351
Q3473
95-th percentile1025
Maximum1948
Range2043
Interquartile range (IQR)200.5

Descriptive statistics

Standard deviation248.287934
Coefficient of variation (CV)0.5862301372
Kurtosis4.824868375
Mean423.5332136
Median Absolute Deviation (MAD)95
Skewness2.054916015
Sum707724
Variance61646.89815
MonotonicityNot monotonic
2023-12-06T17:31:59.699444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266 13
 
< 0.1%
336 11
 
< 0.1%
345 11
 
< 0.1%
355 10
 
< 0.1%
315 10
 
< 0.1%
332 10
 
< 0.1%
354 10
 
< 0.1%
330 9
 
< 0.1%
324 9
 
< 0.1%
241 9
 
< 0.1%
Other values (608) 1569
 
0.1%
(Missing) 1097773
99.8%
ValueCountFrequency (%)
-95 1
< 0.1%
56 1
< 0.1%
59 1
< 0.1%
62 1
< 0.1%
63 2
< 0.1%
ValueCountFrequency (%)
1948 1
< 0.1%
1874 1
< 0.1%
1694 1
< 0.1%
1484 1
< 0.1%
1470 1
< 0.1%

divarrdelay
Real number (ℝ)

MISSING 

Distinct535
Distinct (%)32.0%
Missing1097773
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean266.2315978
Minimum-23
Maximum1717
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size8.4 MiB
2023-12-06T17:31:59.894918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23
5-th percentile75
Q1126.5
median182
Q3266
95-th percentile900
Maximum1717
Range1740
Interquartile range (IQR)139.5

Descriptive statistics

Standard deviation251.8848413
Coefficient of variation (CV)0.9461117438
Kurtosis5.182160503
Mean266.2315978
Median Absolute Deviation (MAD)63
Skewness2.31542705
Sum444873
Variance63445.97328
MonotonicityNot monotonic
2023-12-06T17:32:00.108234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 14
 
< 0.1%
166 14
 
< 0.1%
167 13
 
< 0.1%
116 12
 
< 0.1%
108 12
 
< 0.1%
225 12
 
< 0.1%
149 12
 
< 0.1%
169 11
 
< 0.1%
115 11
 
< 0.1%
146 11
 
< 0.1%
Other values (525) 1549
 
0.1%
(Missing) 1097773
99.8%
ValueCountFrequency (%)
-23 1
< 0.1%
-12 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
24 1
< 0.1%
ValueCountFrequency (%)
1717 1
< 0.1%
1642 1
< 0.1%
1536 1
< 0.1%
1457 1
< 0.1%
1438 1
< 0.1%

divdistance
Real number (ℝ)

MISSING 

Distinct170
Distinct (%)8.1%
Missing1097337
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean44.73327005
Minimum0
Maximum2640
Zeros1671
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:00.314403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile255.9
Maximum2640
Range2640
Interquartile range (IQR)0

Descriptive statistics

Standard deviation166.7707084
Coefficient of variation (CV)3.72811351
Kurtosis108.0699415
Mean44.73327005
Median Absolute Deviation (MAD)0
Skewness8.883206231
Sum94253
Variance27812.46918
MonotonicityNot monotonic
2023-12-06T17:32:00.509282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1671
 
0.2%
89 24
 
< 0.1%
30 21
 
< 0.1%
36 21
 
< 0.1%
47 13
 
< 0.1%
94 13
 
< 0.1%
109 13
 
< 0.1%
213 12
 
< 0.1%
18 11
 
< 0.1%
198 10
 
< 0.1%
Other values (160) 298
 
< 0.1%
(Missing) 1097337
99.8%
ValueCountFrequency (%)
0 1671
0.2%
11 4
 
< 0.1%
17 3
 
< 0.1%
18 11
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
2640 1
< 0.1%
2556 1
< 0.1%
2417 1
< 0.1%
2367 1
< 0.1%
1990 1
< 0.1%

div1airport
Text

MISSING 

Distinct212
Distinct (%)9.2%
Missing1097142
Missing (%)99.8%
Memory size8.4 MiB
2023-12-06T17:32:00.975881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6906
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)2.3%

Sample

1st rowTYS
2nd rowRIC
3rd rowCLE
4th rowAVP
5th rowMKE
ValueCountFrequency (%)
den 141
 
6.1%
lax 92
 
4.0%
ont 86
 
3.7%
dfw 64
 
2.8%
phx 59
 
2.6%
iah 57
 
2.5%
las 54
 
2.3%
slc 53
 
2.3%
ord 49
 
2.1%
atl 44
 
1.9%
Other values (202) 1603
69.6%
2023-12-06T17:32:01.775516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 665
 
9.6%
S 544
 
7.9%
L 521
 
7.5%
D 494
 
7.2%
T 432
 
6.3%
O 382
 
5.5%
N 380
 
5.5%
C 348
 
5.0%
M 315
 
4.6%
I 315
 
4.6%
Other values (16) 2510
36.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6906
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 665
 
9.6%
S 544
 
7.9%
L 521
 
7.5%
D 494
 
7.2%
T 432
 
6.3%
O 382
 
5.5%
N 380
 
5.5%
C 348
 
5.0%
M 315
 
4.6%
I 315
 
4.6%
Other values (16) 2510
36.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 6906
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 665
 
9.6%
S 544
 
7.9%
L 521
 
7.5%
D 494
 
7.2%
T 432
 
6.3%
O 382
 
5.5%
N 380
 
5.5%
C 348
 
5.0%
M 315
 
4.6%
I 315
 
4.6%
Other values (16) 2510
36.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 665
 
9.6%
S 544
 
7.9%
L 521
 
7.5%
D 494
 
7.2%
T 432
 
6.3%
O 382
 
5.5%
N 380
 
5.5%
C 348
 
5.0%
M 315
 
4.6%
I 315
 
4.6%
Other values (16) 2510
36.3%

div1airportid
Real number (ℝ)

MISSING 

Distinct212
Distinct (%)9.2%
Missing1097142
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean12807.19505
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:01.958689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111298
median12892
Q314100
95-th percentile15016
Maximum16869
Range6734
Interquartile range (IQR)2802

Descriptive statistics

Standard deviation1539.911599
Coefficient of variation (CV)0.1202380063
Kurtosis-1.22333838
Mean12807.19505
Median Absolute Deviation (MAD)1370
Skewness-0.01862951605
Sum29482163
Variance2371327.734
MonotonicityNot monotonic
2023-12-06T17:32:02.142717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11292 141
 
< 0.1%
12892 92
 
< 0.1%
13891 86
 
< 0.1%
11298 64
 
< 0.1%
14107 59
 
< 0.1%
12266 57
 
< 0.1%
12889 54
 
< 0.1%
14869 53
 
< 0.1%
13930 49
 
< 0.1%
10397 44
 
< 0.1%
Other values (202) 1603
 
0.1%
(Missing) 1097142
99.8%
ValueCountFrequency (%)
10135 5
 
< 0.1%
10136 4
 
< 0.1%
10140 30
< 0.1%
10146 1
 
< 0.1%
10155 6
 
< 0.1%
ValueCountFrequency (%)
16869 1
 
< 0.1%
16229 1
 
< 0.1%
16101 5
< 0.1%
15991 1
 
< 0.1%
15919 3
< 0.1%

div1airportseqid
Real number (ℝ)

MISSING 

Distinct212
Distinct (%)9.2%
Missing1097142
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean1280723.121
Minimum1013506
Maximum1686901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:02.358560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1013506
5-th percentile1039707
Q11129806
median1289208
Q31410005
95-th percentile1501606
Maximum1686901
Range673395
Interquartile range (IQR)280199

Descriptive statistics

Standard deviation153990.9882
Coefficient of variation (CV)0.1202375328
Kurtosis-1.223340853
Mean1280723.121
Median Absolute Deviation (MAD)136996
Skewness-0.01862885887
Sum2948224624
Variance2.371322445 × 1010
MonotonicityNot monotonic
2023-12-06T17:32:02.542161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1129202 141
 
< 0.1%
1289208 92
 
< 0.1%
1389101 86
 
< 0.1%
1129806 64
 
< 0.1%
1410702 59
 
< 0.1%
1226603 57
 
< 0.1%
1288903 54
 
< 0.1%
1486903 53
 
< 0.1%
1393007 49
 
< 0.1%
1039707 44
 
< 0.1%
Other values (202) 1603
 
0.1%
(Missing) 1097142
99.8%
ValueCountFrequency (%)
1013506 5
 
< 0.1%
1013603 4
 
< 0.1%
1014005 30
< 0.1%
1014602 1
 
< 0.1%
1015502 6
 
< 0.1%
ValueCountFrequency (%)
1686901 1
 
< 0.1%
1622903 1
 
< 0.1%
1610102 5
< 0.1%
1599102 1
 
< 0.1%
1591904 3
< 0.1%

div1wheelson
Real number (ℝ)

MISSING 

Distinct998
Distinct (%)43.4%
Missing1097142
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean1443.234144
Minimum2
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:02.742535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile133.05
Q11030
median1438
Q31957
95-th percentile2301.95
Maximum2400
Range2398
Interquartile range (IQR)927

Descriptive statistics

Standard deviation595.933336
Coefficient of variation (CV)0.4129152144
Kurtosis-0.3515567787
Mean1443.234144
Median Absolute Deviation (MAD)470
Skewness-0.4498463383
Sum3322325
Variance355136.541
MonotonicityNot monotonic
2023-12-06T17:32:02.959573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1029 9
 
< 0.1%
2120 8
 
< 0.1%
933 7
 
< 0.1%
2140 7
 
< 0.1%
2207 7
 
< 0.1%
2016 7
 
< 0.1%
1031 6
 
< 0.1%
2240 6
 
< 0.1%
2039 6
 
< 0.1%
2218 6
 
< 0.1%
Other values (988) 2233
 
0.2%
(Missing) 1097142
99.8%
ValueCountFrequency (%)
2 3
< 0.1%
3 1
 
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
7 2
< 0.1%
ValueCountFrequency (%)
2400 1
 
< 0.1%
2358 3
< 0.1%
2357 1
 
< 0.1%
2356 2
< 0.1%
2355 3
< 0.1%

div1totalgtime
Real number (ℝ)

MISSING 

Distinct151
Distinct (%)6.6%
Missing1097142
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean31.74847958
Minimum1
Maximum266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:03.125415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q113
median21
Q339
95-th percentile99.95
Maximum266
Range265
Interquartile range (IQR)26

Descriptive statistics

Standard deviation30.32040526
Coefficient of variation (CV)0.9550191271
Kurtosis5.552218541
Mean31.74847958
Median Absolute Deviation (MAD)11
Skewness2.095475573
Sum73085
Variance919.3269751
MonotonicityNot monotonic
2023-12-06T17:32:03.309508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 78
 
< 0.1%
4 77
 
< 0.1%
13 76
 
< 0.1%
17 75
 
< 0.1%
5 73
 
< 0.1%
16 73
 
< 0.1%
14 67
 
< 0.1%
19 65
 
< 0.1%
15 64
 
< 0.1%
11 63
 
< 0.1%
Other values (141) 1591
 
0.1%
(Missing) 1097142
99.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 14
 
< 0.1%
3 28
 
< 0.1%
4 77
< 0.1%
5 73
< 0.1%
ValueCountFrequency (%)
266 1
 
< 0.1%
218 1
 
< 0.1%
203 1
 
< 0.1%
192 1
 
< 0.1%
169 3
< 0.1%

div1longestgtime
Real number (ℝ)

MISSING 

Distinct141
Distinct (%)6.1%
Missing1097142
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean25.81494353
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:03.513025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median15
Q329
95-th percentile91
Maximum252
Range251
Interquartile range (IQR)19

Descriptive statistics

Standard deviation27.93922415
Coefficient of variation (CV)1.082288796
Kurtosis6.832991602
Mean25.81494353
Median Absolute Deviation (MAD)7
Skewness2.376252758
Sum59426
Variance780.600246
MonotonicityNot monotonic
2023-12-06T17:32:03.716205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 131
 
< 0.1%
12 124
 
< 0.1%
13 113
 
< 0.1%
9 107
 
< 0.1%
10 104
 
< 0.1%
14 97
 
< 0.1%
7 93
 
< 0.1%
5 83
 
< 0.1%
8 82
 
< 0.1%
4 79
 
< 0.1%
Other values (131) 1289
 
0.1%
(Missing) 1097142
99.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 14
 
< 0.1%
3 31
 
< 0.1%
4 79
< 0.1%
5 83
< 0.1%
ValueCountFrequency (%)
252 1
< 0.1%
209 1
< 0.1%
192 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%

div1wheelsoff
Real number (ℝ)

MISSING 

Distinct860
Distinct (%)50.8%
Missing1097752
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean1408.33156
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:03.924662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile128.1
Q11104
median1358
Q31833.25
95-th percentile2302.45
Maximum2400
Range2399
Interquartile range (IQR)729.25

Descriptive statistics

Standard deviation574.9856353
Coefficient of variation (CV)0.4082743379
Kurtosis0.02678626034
Mean1408.33156
Median Absolute Deviation (MAD)356
Skewness-0.4658804332
Sum2382897
Variance330608.4808
MonotonicityNot monotonic
2023-12-06T17:32:04.125244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1308 7
 
< 0.1%
1243 7
 
< 0.1%
954 6
 
< 0.1%
936 6
 
< 0.1%
1121 6
 
< 0.1%
1235 6
 
< 0.1%
1134 6
 
< 0.1%
1410 6
 
< 0.1%
1354 6
 
< 0.1%
1732 5
 
< 0.1%
Other values (850) 1631
 
0.1%
(Missing) 1097752
99.8%
ValueCountFrequency (%)
1 3
< 0.1%
3 4
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
< 0.1%
ValueCountFrequency (%)
2400 2
< 0.1%
2359 1
< 0.1%
2356 2
< 0.1%
2353 1
< 0.1%
2352 2
< 0.1%

div1tailnum
Text

MISSING 

Distinct1385
Distinct (%)81.9%
Missing1097752
Missing (%)99.8%
Memory size8.4 MiB
2023-12-06T17:32:04.624584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.978132388
Min length5

Characters and Unicode

Total characters10115
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1111 ?
Unique (%)65.7%

Sample

1st rowN603NN
2nd rowN607NN
3rd rowN533AE
4th rowN535EA
5th rowN551NN
ValueCountFrequency (%)
n706tw 5
 
0.3%
n220wn 4
 
0.2%
n182sy 4
 
0.2%
n403yx 3
 
0.2%
n208sy 3
 
0.2%
n598jb 3
 
0.2%
n7840a 3
 
0.2%
n935ae 3
 
0.2%
n612ae 3
 
0.2%
n271sy 3
 
0.2%
Other values (1375) 1658
98.0%
2023-12-06T17:32:05.290933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2209
21.8%
8 614
 
6.1%
3 606
 
6.0%
7 589
 
5.8%
2 588
 
5.8%
9 560
 
5.5%
5 534
 
5.3%
4 532
 
5.3%
1 528
 
5.2%
6 525
 
5.2%
Other values (24) 2830
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5484
54.2%
Uppercase Letter 4631
45.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2209
47.7%
A 354
 
7.6%
S 348
 
7.5%
W 266
 
5.7%
J 172
 
3.7%
Y 152
 
3.3%
B 146
 
3.2%
U 126
 
2.7%
K 124
 
2.7%
D 121
 
2.6%
Other values (14) 613
 
13.2%
Decimal Number
ValueCountFrequency (%)
8 614
11.2%
3 606
11.1%
7 589
10.7%
2 588
10.7%
9 560
10.2%
5 534
9.7%
4 532
9.7%
1 528
9.6%
6 525
9.6%
0 408
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5484
54.2%
Latin 4631
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2209
47.7%
A 354
 
7.6%
S 348
 
7.5%
W 266
 
5.7%
J 172
 
3.7%
Y 152
 
3.3%
B 146
 
3.2%
U 126
 
2.7%
K 124
 
2.7%
D 121
 
2.6%
Other values (14) 613
 
13.2%
Common
ValueCountFrequency (%)
8 614
11.2%
3 606
11.1%
7 589
10.7%
2 588
10.7%
9 560
10.2%
5 534
9.7%
4 532
9.7%
1 528
9.6%
6 525
9.6%
0 408
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2209
21.8%
8 614
 
6.1%
3 606
 
6.0%
7 589
 
5.8%
2 588
 
5.8%
9 560
 
5.5%
5 534
 
5.3%
4 532
 
5.3%
1 528
 
5.2%
6 525
 
5.2%
Other values (24) 2830
28.0%

div2airport
Text

MISSING 

Distinct19
Distinct (%)70.4%
Missing1099417
Missing (%)> 99.9%
Memory size8.4 MiB
2023-12-06T17:32:05.524286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)59.3%

Sample

1st rowTLH
2nd rowMLI
3rd rowTWF
4th rowLAX
5th rowLAX
ValueCountFrequency (%)
dfw 6
22.2%
lax 3
 
11.1%
pie 2
 
7.4%
tlh 1
 
3.7%
mli 1
 
3.7%
twf 1
 
3.7%
iah 1
 
3.7%
den 1
 
3.7%
aza 1
 
3.7%
fll 1
 
3.7%
Other values (9) 9
33.3%
2023-12-06T17:32:05.874196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 10
12.3%
D 8
9.9%
A 8
9.9%
F 8
9.9%
W 7
 
8.6%
I 6
 
7.4%
T 5
 
6.2%
P 4
 
4.9%
C 3
 
3.7%
U 3
 
3.7%
Other values (10) 19
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 10
12.3%
D 8
9.9%
A 8
9.9%
F 8
9.9%
W 7
 
8.6%
I 6
 
7.4%
T 5
 
6.2%
P 4
 
4.9%
C 3
 
3.7%
U 3
 
3.7%
Other values (10) 19
23.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 10
12.3%
D 8
9.9%
A 8
9.9%
F 8
9.9%
W 7
 
8.6%
I 6
 
7.4%
T 5
 
6.2%
P 4
 
4.9%
C 3
 
3.7%
U 3
 
3.7%
Other values (10) 19
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 10
12.3%
D 8
9.9%
A 8
9.9%
F 8
9.9%
W 7
 
8.6%
I 6
 
7.4%
T 5
 
6.2%
P 4
 
4.9%
C 3
 
3.7%
U 3
 
3.7%
Other values (10) 19
23.5%

div2airportid
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)70.4%
Missing1099417
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean12676.88889
Minimum10423
Maximum15389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:06.040968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10423
5-th percentile10643.3
Q111298
median12889
Q314097
95-th percentile15333.7
Maximum15389
Range4966
Interquartile range (IQR)2799

Descriptive statistics

Standard deviation1532.202521
Coefficient of variation (CV)0.1208658161
Kurtosis-1.075100109
Mean12676.88889
Median Absolute Deviation (MAD)1591
Skewness0.3348815238
Sum342276
Variance2347644.564
MonotonicityNot monotonic
2023-12-06T17:32:06.193710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
11298 6
 
< 0.1%
12892 3
 
< 0.1%
14112 2
 
< 0.1%
14122 1
 
< 0.1%
14524 1
 
< 0.1%
12191 1
 
< 0.1%
10423 1
 
< 0.1%
15370 1
 
< 0.1%
13204 1
 
< 0.1%
12889 1
 
< 0.1%
Other values (9) 9
 
< 0.1%
(Missing) 1099417
> 99.9%
ValueCountFrequency (%)
10423 1
 
< 0.1%
10466 1
 
< 0.1%
11057 1
 
< 0.1%
11292 1
 
< 0.1%
11298 6
< 0.1%
ValueCountFrequency (%)
15389 1
< 0.1%
15370 1
< 0.1%
15249 1
< 0.1%
14524 1
< 0.1%
14122 1
< 0.1%

div2airportseqid
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)70.4%
Missing1099417
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1267693.222
Minimum1042302
Maximum1538902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:06.342203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1042302
5-th percentile1064332.3
Q11129806
median1288903
Q31409704
95-th percentile1533373.2
Maximum1538902
Range496600
Interquartile range (IQR)279898

Descriptive statistics

Standard deviation153219.9266
Coefficient of variation (CV)0.1208651461
Kurtosis-1.075098842
Mean1267693.222
Median Absolute Deviation (MAD)159097
Skewness0.3348669168
Sum34227717
Variance2.34763459 × 1010
MonotonicityNot monotonic
2023-12-06T17:32:06.507355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1129806 6
 
< 0.1%
1289208 3
 
< 0.1%
1411206 2
 
< 0.1%
1412202 1
 
< 0.1%
1452401 1
 
< 0.1%
1219102 1
 
< 0.1%
1042302 1
 
< 0.1%
1537002 1
 
< 0.1%
1320402 1
 
< 0.1%
1288903 1
 
< 0.1%
Other values (9) 9
 
< 0.1%
(Missing) 1099417
> 99.9%
ValueCountFrequency (%)
1042302 1
 
< 0.1%
1046602 1
 
< 0.1%
1105703 1
 
< 0.1%
1129202 1
 
< 0.1%
1129806 6
< 0.1%
ValueCountFrequency (%)
1538902 1
< 0.1%
1537002 1
< 0.1%
1524906 1
< 0.1%
1452401 1
< 0.1%
1412202 1
< 0.1%

div2wheelson
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)100.0%
Missing1099417
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1058.074074
Minimum13
Maximum2338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:06.677283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile49.9
Q1147
median1218
Q31833.5
95-th percentile2310.9
Maximum2338
Range2325
Interquartile range (IQR)1686.5

Descriptive statistics

Standard deviation873.4819326
Coefficient of variation (CV)0.8255394911
Kurtosis-1.748354273
Mean1058.074074
Median Absolute Deviation (MAD)917
Skewness0.04602865
Sum28568
Variance762970.6866
MonotonicityNot monotonic
2023-12-06T17:32:06.857379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1145 1
 
< 0.1%
243 1
 
< 0.1%
1818 1
 
< 0.1%
1400 1
 
< 0.1%
49 1
 
< 0.1%
1218 1
 
< 0.1%
1938 1
 
< 0.1%
2313 1
 
< 0.1%
2306 1
 
< 0.1%
301 1
 
< 0.1%
Other values (17) 17
 
< 0.1%
(Missing) 1099417
> 99.9%
ValueCountFrequency (%)
13 1
< 0.1%
49 1
< 0.1%
52 1
< 0.1%
57 1
< 0.1%
101 1
< 0.1%
ValueCountFrequency (%)
2338 1
< 0.1%
2313 1
< 0.1%
2306 1
< 0.1%
2015 1
< 0.1%
1953 1
< 0.1%

div2totalgtime
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)63.0%
Missing1099417
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean21.77777778
Minimum4
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:07.040687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.3
Q17
median10
Q325
95-th percentile67.1
Maximum98
Range94
Interquartile range (IQR)18

Descriptive statistics

Standard deviation24.56754165
Coefficient of variation (CV)1.128101402
Kurtosis2.693810692
Mean21.77777778
Median Absolute Deviation (MAD)3
Skewness1.839666005
Sum588
Variance603.5641026
MonotonicityNot monotonic
2023-12-06T17:32:07.240983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 7
 
< 0.1%
10 4
 
< 0.1%
11 2
 
< 0.1%
15 1
 
< 0.1%
98 1
 
< 0.1%
41 1
 
< 0.1%
4 1
 
< 0.1%
27 1
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
Other values (7) 7
 
< 0.1%
(Missing) 1099417
> 99.9%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 7
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
98 1
< 0.1%
68 1
< 0.1%
65 1
< 0.1%
63 1
< 0.1%
41 1
< 0.1%

div2longestgtime
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)63.0%
Missing1099417
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean19.85185185
Minimum4
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:07.440780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.3
Q17
median10
Q322.5
95-th percentile59.8
Maximum98
Range94
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation22.23424888
Coefficient of variation (CV)1.120008806
Kurtosis5.150273981
Mean19.85185185
Median Absolute Deviation (MAD)3
Skewness2.232024957
Sum536
Variance494.3618234
MonotonicityNot monotonic
2023-12-06T17:32:07.625728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 7
 
< 0.1%
10 4
 
< 0.1%
11 2
 
< 0.1%
15 1
 
< 0.1%
98 1
 
< 0.1%
41 1
 
< 0.1%
4 1
 
< 0.1%
27 1
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
Other values (7) 7
 
< 0.1%
(Missing) 1099417
> 99.9%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 7
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
98 1
< 0.1%
61 1
< 0.1%
57 1
< 0.1%
44 1
< 0.1%
41 1
< 0.1%

div2wheelsoff
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)100.0%
Missing1099438
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1447.333333
Minimum1043
Maximum1956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2023-12-06T17:32:07.806813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1043
5-th percentile1111
Q11338.5
median1432
Q31493.25
95-th percentile1843.5
Maximum1956
Range913
Interquartile range (IQR)154.75

Descriptive statistics

Standard deviation298.2312302
Coefficient of variation (CV)0.2060556634
Kurtosis2.06749471
Mean1447.333333
Median Absolute Deviation (MAD)95.5
Skewness0.7163663845
Sum8684
Variance88941.86667
MonotonicityNot monotonic
2023-12-06T17:32:07.957688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1506 1
 
< 0.1%
1455 1
 
< 0.1%
1409 1
 
< 0.1%
1043 1
 
< 0.1%
1956 1
 
< 0.1%
1315 1
 
< 0.1%
(Missing) 1099438
> 99.9%
ValueCountFrequency (%)
1043 1
< 0.1%
1315 1
< 0.1%
1409 1
< 0.1%
1455 1
< 0.1%
1506 1
< 0.1%
ValueCountFrequency (%)
1956 1
< 0.1%
1506 1
< 0.1%
1455 1
< 0.1%
1409 1
< 0.1%
1315 1
< 0.1%

div2tailnum
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing1099438
Missing (%)> 99.9%
Memory size8.4 MiB
2023-12-06T17:32:08.175655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters36
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowN613NN
2nd rowN869AS
3rd rowN909SW
4th rowN27255
5th rowN924US
ValueCountFrequency (%)
n613nn 1
16.7%
n869as 1
16.7%
n909sw 1
16.7%
n27255 1
16.7%
n924us 1
16.7%
n808nn 1
16.7%
2023-12-06T17:32:08.589936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 10
27.8%
9 4
 
11.1%
8 3
 
8.3%
S 3
 
8.3%
2 3
 
8.3%
6 2
 
5.6%
0 2
 
5.6%
5 2
 
5.6%
1 1
 
2.8%
3 1
 
2.8%
Other values (5) 5
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
55.6%
Uppercase Letter 16
44.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 4
20.0%
8 3
15.0%
2 3
15.0%
6 2
10.0%
0 2
10.0%
5 2
10.0%
1 1
 
5.0%
3 1
 
5.0%
7 1
 
5.0%
4 1
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 10
62.5%
S 3
 
18.8%
A 1
 
6.2%
W 1
 
6.2%
U 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20
55.6%
Latin 16
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9 4
20.0%
8 3
15.0%
2 3
15.0%
6 2
10.0%
0 2
10.0%
5 2
10.0%
1 1
 
5.0%
3 1
 
5.0%
7 1
 
5.0%
4 1
 
5.0%
Latin
ValueCountFrequency (%)
N 10
62.5%
S 3
 
18.8%
A 1
 
6.2%
W 1
 
6.2%
U 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 10
27.8%
9 4
 
11.1%
8 3
 
8.3%
S 3
 
8.3%
2 3
 
8.3%
6 2
 
5.6%
0 2
 
5.6%
5 2
 
5.6%
1 1
 
2.8%
3 1
 
2.8%
Other values (5) 5
13.9%

div3airport
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3airportid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3airportseqid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3wheelson
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3totalgtime
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3longestgtime
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3wheelsoff
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div3tailnum
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4airport
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4airportid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4airportseqid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4wheelson
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4totalgtime
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4longestgtime
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4wheelsoff
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div4tailnum
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5airport
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5airportid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5airportseqid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5wheelson
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5totalgtime
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5longestgtime
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5wheelsoff
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

div5tailnum
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB

empty_col
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1099444
Missing (%)100.0%
Memory size8.4 MiB